Method of Predicting Personalized Response to Cancer Therapy, Method of Treating Cancer, and Kit Therefor

ABSTRACT

A method and a kit are provided for predicting a favorable or a non-favorable response of a cancer patient to treatment with a cancer therapy by determining in a biological sample obtained from the cancer patient, before and after the treatment, the changes of the levels of factors/biomarkers generated by the cancer patient in response to said treatment, and a method for treatment of a cancer patient.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of International Application No. PCT/IL2018/050608, filed Jun. 4, 2018, in which the United States is designated, and is a non-provisional of the Provisional Application No. 62/594,141, filed Dec. 4, 2017, and is a non-provisional of the Provisional Application No. 62/564,392, filed Sep. 28, 2017, and is a non-provisional of the Provisional Application No. 62/514,851, filed Jun. 4, 2017, the entire contents of each and all these applications being hereby incorporated by reference herein in their entirety as if fully disclosed herein.

FIELD OF THE INVENTION

The present invention is in the field of oncology and particularly relates to a method of predicting a personalized response of a cancer patient to treatment with a cancer therapy, to kits therefor, and to a method of treatment of a cancer patient with a cancer therapy.

BACKGROUND

One of the major obstacles in clinical oncology is that tumors often develop resistance to therapy even when an initial tumor response to treatment is observed. Many studies have focused on the contribution of mutations and genetic aberrations in the tumor cells which promote drug resistance and can explain tumor re-growth. However, studies have demonstrated that the host, in response to cancer therapy, generates pro-tumorigenic and pro-metastatic effects which in turn contribute to tumor re-growth, and therefore negate the anti-tumor activity of the drug (for reviews see Katz and Shaked, 2015; Shaked, 2016).

Host-mediated responses to anti-cancer treatment modalities may be molecular and/or cellular responses. Upon treatment with chemotherapeutic drugs, host bone marrow derived cells (BMDCs) are mobilized from the bone marrow compartment, colonize the treated tumor and contribute to tumor angiogenesis and cancer re-growth, thereby promoting therapy resistance (Shaked et al., 2006, 2008). Cancer therapy also induces pro-tumorigenic activation of various immune cells such as macrophages and antigen presenting cells (Beyar-Katz et al., 2016; De Palma and Lewis, 2013; Kim et al. 2012; Ma et al., 2013). Overall, these aforementioned studies indicate that host-mediated molecular and cellular responses to different anti-cancer treatments involve the activation or education of immune cells as well as the secretion of various pro-tumorigenic factors. These combined effects contribute to tumor re-growth and resistance to therapy. This relatively new phenomenon has made a paradigm shift in understanding cancer progression and resistance to therapy.

Recently, a new treatment modality, an immunotherapy using immune checkpoint inhibitors (ICIs), is revolutionizing cancer therapy. Such immune-modulating drugs have shown remarkable successes for the treatment of advanced malignancies (including stage IV) such as melanoma, prostate, non-small cell lung cancer, renal cell carcinoma and also some hematological malignancies (Postow et al., 2015). Although the human immune system is capable of recognizing and mounting a response to cancerous cells, this response is often circumvented by tumor-derived inhibition resulting in immune tolerance. In this regard, tumor-infiltrating lymphocytes (TILs), such as tumor antigen-specific CD8⁺ cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells, have been found to colonize the tumor microenvironment (Gajewski et al., 2013). Yet, at the tumor site, they completely lack the ability to act against tumor cells (Ostrand-Rosenberg and Sinha, 2009). This is due to direct inhibitory effects of factors secreted by cancer cells, stromal cells or other suppressive immune cells such as myeloid derived suppressor cells (MDSCs) and T regulatory cells (Tregs) (Makkouk and Weiner, 2015). For instance, IL-10 is frequently upregulated in various types of cancer, and was shown to suppress the immune system (Sato et al., 2011). Thus, identifying molecules that negatively regulate the immune system against tumor cells, will lead to the development of immunomodulatory drugs that support the activation of immune cells against tumors.

Of specific interest are immune checkpoint proteins, such as CTLA-4, PD-1 and its ligand, PD-L1. These checkpoint proteins are expressed by tumor cells or other immune cells and contribute to the exhaustion of CTLs (Postow et al., 2015; Topalian et al., 2015). Specifically, they keep immune responses in check, and inhibit T cell killing effects against tumor cells. As such, checkpoint inhibitors have been developed in order to inhibit the immune suppression effects. Currently, antibodies blocking the immune checkpoints, CTLA-4 and PD-1 or its ligand PD-L1 have been developed (Pardoll, 2012). These ICIs are currently in use in the clinic for the treatment of various malignancies with some promising and remarkable successes (Romano and Romero, 2015). However, ICIs have shown therapeutic benefit only for a limited portion of cancer patients (˜10-20%). For example, pooled data from clinical studies of ipilimumab, a CTLA-4 blocking antibody, revealed that the duration of clinical response is around 3 years, and can last up to 10 years. However, this dramatic therapeutic effect is only observed in a subset of patients (˜20%). Thus, the majority of patients exhibit intrinsic resistance mechanisms to such therapies. Yet, the molecular aspects that define the subpopulation of patients that are responsive to ICIs are not fully clear. It has been suggested that markers, such as PD-L1 expression by tumor cells, mutational burden, and lymphocytic infiltrates could predict the cancer patients that will respond to immunotherapy. However, these aforementioned biomarkers do not always correlate with tumor responsiveness to immunotherapy or resistance of patients to ICIs. Therefore, additional possible mechanisms are still unknown.

It would be highly desirable to unveil host-mediated cellular and molecular mechanisms that contribute to tumor resistance to all modalities of cancer therapy including the promising ICI therapy modality. This will permit development of strategies to block such unwanted host effects and will improve therapeutic outcome and delay resistance to cancer therapy.

SUMMARY OF THE INVENTION

In one aspect, the present invention relates to a method for identification of a set of host-driven resistance factors to a cancer therapy in a biological sample of a cancer patient treated with said cancer therapy. These factors are Specific Host-Driven Resistance Factors, namely, they are not generated by intrinsic resistance of the cancer cells, but are driven by the host, i.e., the cancer patient, in response to said determined cancer therapy, and may limit or counteract the effectiveness of the treatment with the cancer therapy modality/drugs applied to said patient. The determination of these factors allows the prediction in a personalized form of the favorable or non-favorable response of the patient to the treatment with the cancer therapy modality/drugs. These factors, herein designated interchangeably “factors” or “biomarkers”, are factors, mainly cytokines, chemokines, growth factors, soluble receptors, enzymes and other molecules produced by the host cells, either at different organs or at the tumor microenvironment, in response to the cancer therapy with which the patient is treated.

Thus, in certain embodiments, the present invention relates to a method for predicting the response of a cancer patient to treatment with a cancer therapy, comprising: determining in a biological sample obtained from the cancer patient at a time period after a session of treatment with said cancer therapy the levels of a plurality of factors generated by the cancer patient in response to said cancer therapy, one or more of the plurality of factors promoting responsiveness or non-responsiveness of the patient to said cancer therapy, wherein a change in the levels of two or more of the plurality of factors as compared to a reference level, predicts a favorable or a non-favorable response of the cancer patient to the said cancer therapy.

In certain embodiments, the biological sample is blood plasma. In certain embodiments, the biological sample of the cancer patient is a whole blood sample. In certain embodiments, the biological sample is blood serum. In certain embodiments, the biological sample is peripheral blood mononuclear cells.

In certain embodiments, the present invention is directed to a method for predicting the response of a cancer patient to treatment with a cancer therapy, the method comprising the steps of:

-   -   (i) performing an assay on a biological sample selected from         blood plasma, whole blood, blood serum or peripheral blood         mononuclear cells obtained from the cancer patient at a time         period after a session of treatment with said cancer therapy, to         determine the levels of one or more of a plurality of factors         induced in the circulation of said cancer patient in response to         treatment with said cancer therapy, said one or more of the         plurality of factors promoting responsiveness or         non-responsiveness of the cancer patient to the treatment with         said cancer therapy;     -   (ii) obtaining reference levels for each of the one or more of         the plurality of the induced factors of step (i) in a biological         sample selected from blood plasma, whole blood, blood serum or         peripheral blood mononuclear cells, obtained from the cancer         patient before said session of treatment with the cancer         therapy;     -   (iii) establishing the fold change for each of the one or more         of the plurality of the induced factors of step (i) by comparing         the level of each induced factor of step (i) with the reference         level of step (ii) for the same factor; and     -   (iv) determining that the cancer patient has a favorable or a         non-favorable response to the treatment with said cancer therapy         based on the fold change established in step (iii) for one or         more of the plurality of induced factors of step (i).

In another aspect, the present invention provides a kit comprising a plurality of antibodies, each antibody of the plurality of antibodies selectively binding to each of a plurality of factors that promote responsiveness or non-responsiveness of a cancer patient to treatment with a cancer therapy, and instructions for use.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 demonstrates that the host response score has predictive value for patient outcome.

FIGS. 2A-B show host-induction of IL-6 in response to chemotherapeutic treatment and the effect of blocking IL-6 in the treatment with the chemotherapeutic agent. 2A shows that treatment with 240 □g doxorubicin (DOX) caused an increased plasma level of IL-6 in BALB/c mice. 2B shows that treatment with Doxorubicin in combination with anti-IL-6 (squares) resulted in improved anti-tumor effect compared to control (circles), with Doxorubicin (diamonds), or anti-IL-6 (triangles).

DETAILED DESCRIPTION

Before describing the methods and kits of the invention, it should be understood that this invention is not limited to the particular methodology and protocols as described herein. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments of the invention only and, if not defined otherwise, it is not intended to limit the scope of the present invention which will be recited in the appended claims.

It must also be noted that as used herein and in the appended claims, the singular form “a”, “an”, and “the” include plural reference unless the context clearly dictates otherwise.

As used herein, the term “a cancer therapy” may be used interchangeably with the term “a cancer therapy modality”, and include plural reference, namely, one single modality therapy or a combination of two or more modality therapies.

As used herein, the terms “induced”, “driven” and “generated” are used interchangeably to denote the factors induced into the circulation by the cancer patient in response to the cancer therapy (“host-response”).

In accordance with the invention, the cancer therapy is related to treatment of all types of cancer, primary or metastatic, selected from sarcomas, carcinomas, myelomas, lymphomas and leukemias. In certain embodiments, the cancer is of the sarcoma type, e.g. soft tissue sarcoma, osteosarcoma.

In certain embodiments, the cancer is a carcinoma including, but without being limited to, melanoma, brain, head, neck, bone, nasopharyngeal, liver, gastrointestinal, biliary, bile duct, esophageal, colon, rectal, colorectal, ovarian, breast, cervical, prostate, renal, penile, testicular, skin, lung, skin, lung, chest, pancreatic, thymus, thyroid, or bladder cancer.

In certain embodiments, the cancer is a lymphoma, a cancer of the lymphatic system that may be a Hodgkin lymphoma or a non-Hodgkin lymphoma, either B-cell lymphoma or T-cell lymphoma.

In certain embodiments, the cancer is leukemia, that may be acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL) or chronic myeloid leukemia (CML). In certain embodiments, the cancer is multiple myeloma.

As used herein, the terms “a cancer therapy” and “a cancer therapy modality” refer to any modality of cancer therapy including, but without being limited to, chemotherapy, radiation therapy, surgery, targeted therapy (including all types of immunotherapy, anti-angiogenic therapy, hormonal therapy, and photodynamic therapy), thermotherapy, and combinations thereof.

In certain embodiments, the cancer therapy is adjuvant therapy, namely, an additional cancer treatment given after the main/primary treatment, which is usually surgery, to lower the risk of recurrence of the cancer. Examples of adjuvant therapy include chemotherapy, radiation therapy, hormone therapy, targeted therapy,

In certain embodiments, the cancer therapy is neoadjuvant therapy, namely, a cancer treatment given as a first step to shrink a tumor before the main/primary treatment, which is usually surgery, is given. Examples of neoadjuvant therapy include chemotherapy, radiation therapy, and hormone therapy.

In certain embodiments, the cancer therapy modality is chemotherapy with chemotherapeutic drugs that target and kill cells that quickly grow and divide, as cancer cells do, but can also affect some fast-growing healthy cells. In certain embodiments, chemotherapy is used as the single treatment. In certain other embodiments, chemotherapy is used in combination with another cancer therapy such as surgery, radiation therapy or targeted therapy.

In certain embodiments, the chemotherapy is mono-chemotherapy, i.e., treatment with one single chemotherapy drug including paclitaxel, 5-fluorouracil, doxorubicin, gemcitabine and cyclophosphamide.

In certain other embodiments, the chemotherapy is combination chemotherapy, i.e., treatment with two, three, four or more chemotherapy drugs.

Hereinafter, the name of a drug presented within brackets with an initial capital letter refers to a brand name of the drug. For example, (Taxol) is a brand name for paclitaxel, and could be presented also as TAXOL or TAXOL®.

In certain embodiments, for treatment of breast cancer, for example, chemotherapy for adjuvant and neoadjuvant chemotherapy is carried out with a combination of 2 or 3 drugs chosen from: (i) anthracyclines including doxorubicin (Adriamycin), and epirubicin (Ellence); (ii) taxanes including paclitaxel (Taxol), and docetaxel (Taxotere); (iii) 5-fluorouracil (5-FU); (iv) cyclophosphamide (Cytoxan); and (v) platinum agents including carboplatin (Paraplatin). In certain embodiments, treatment of breast cancer is carried out with paclitaxel. In certain other embodiments, treatment of breast cancer is carried out with the combination paclitaxel/carboplatin. In certain other embodiments, treatment is carried out with the combination Adriamycin/Cyclophosphamide (AC).

In certain embodiments, for treatment of advanced breast cancer that has spread, adjuvant chemotherapy is carried out with one single chemo drug or a combination of 2 or 3 drugs chosen from: (i) anthracyclines such as doxorubicin, pegylated liposomal doxorubicin, and epirubicin: (ii) taxanes such as paclitaxel, docetaxel and albumin-bound paclitaxel; (iii) platinum agents such as cisplatin (Platinol), oxaliplatin and carboplatin; (iv) vinorelbine (Navelbine); (v) capecitabine (Xeloda); (vi) gemcitabine (Gemzar); (vii) ixabepilone; and (viii) eribulin (Halaven).

In certain embodiments, for treatment of bowel, colon or colorectal cancer, adjuvant chemotherapy is carried out with one or more drugs chosen from 5-fluorouracil (5-FU), leucovorin, capecitabine, irinotecan (Camptosar), oxaliplatin (Eloxatin) or a combination of trifluridine and tipiracil (Lonsurf) depending on the stage of the cancer. In certain embodiments, a combination of 2 to 4 of chemo drugs is chosen such as FOLFOX (5-FU+leucovorin+oxaliplatin), FOLFIRI (5-FU+leucovorin+irinotecan), FOLFOXIRI (5-FU+leucovorin+oxaliplatin+irinotecan), or CAPEOX (capecitabine+oxaliplatin) or capecitabine alone may be used.

In certain embodiments, treatment of testicular cancer is carried out with a combination of the chemotherapy drugs cisplatin, etoposide and ifosfamide (PEI).

In certain embodiments, the cancer therapy modality is radiation therapy with high-energy radiation, e.g., x-rays, gamma rays, electron beams, or protons, to shrink tumors and destroy or damage cancer cells thus preventing them from growing and dividing.

In certain embodiments, the cancer therapy modality is surgery for removal of localized cancerous solid tumors and surrounding tissue during an operation. Surgery may be the curative treatment or the primary treatment in combination with chemotherapy or radiation therapy prior to, or after, surgery.

In certain embodiments, the cancer therapy is targeted cancer therapy, sometimes called “molecularly targeted drugs” or “molecularly targeted therapies”. These therapies use drugs or other substances to identify and attack specific types of cancer cells with less harm to normal cells. Some targeted therapies block the growth and spread of cancer by interfering with specific molecules (“molecular targets”), e.g., enzymes or proteins found in cancer cells or in cells related to cancer growth, like blood vessels. In this way, the therapy targets molecules involved in the growth, progression, and spread of cancer cells, rather than simply interfering with all rapidly dividing cells as in traditional chemotherapy. Some targeted therapies are often cytostatic, namely, they block tumor cell proliferation, while standard chemotherapy agents are cytotoxic, and namely kill tumor cells. Other types of targeted therapies help the immune system kill cancer cells or deliver toxic substances directly to the cancer cells and kill them.

A good target is a target that plays a key role in cancer cell growth and survival. For example, proteins present in cancer cells but not in normal cells or proteins more abundant in cancer cells are potential good targets, particularly if they are known to be involved in cell growth or survival. An example is the human epidermal growth factor receptor 2 protein (HER-2) that is expressed at high levels on the surface of some cancer cells in breast and stomach tumors. Another example is cell growth signaling protein BRAF present in an altered form (BRAF V600E) in many melanomas. A further example is the creation of a fusion gene by chromosome abnormalities whose protein may drive cancer development, such as the BCR-ABL fusion protein present in some leukemia cells.

The main types of targeted therapy are small-molecule drugs and monoclonal antibodies.

In certain embodiments, the cancer therapy is targeted therapy with small-molecule drugs that enter cells easily and reach targets that are inside the cells.

In certain embodiments, the small molecules are proteasome inhibitors that block the action of proteasomes, cellular complexes that break down proteins. In certain embodiments, the proteasome inhibitors include, but not limited to, bortezomib (Velcade), carfilzomib (Kyprolis) and Ixazomib (Ninlaro), all approved for treatment of multiple myeloma.

In certain embodiments, the small molecules are receptor tyrosine-kinase inhibitors (TKI) that inhibit the phosphorylation of the tyrosine kinases enzymes responsible for the activation of many proteins by signal transduction cascades. In certain embodiments, the TKIs include, but are not limited to: dasatinib (Sprycel) that targets BCR-ABL and other kinases and was approved for treatment of CML; erlotinib (Tarceva) and gefitinib (Iressa) that target EGFR and approved for non-small cell lung cancer; imatinib mesylate (Gleevec) that targets the BCR-ABL fusion protein and was approved for treatment of CLL and gastrointestinal stromal tumor; lapatinib (Tykerb); nilotinib (Tarsigna), for treatment of CML; pazopanib (Votrient), that blocks tumor growth and inhibits angiogenesis, for treatment of advanced renal cell carcinoma (RCC); sorafenib (Nexavar) for treatment of RCC and hepatocellular carcinoma (HCC); and sunitinib (Sutent) approved for metastatic RCC.

In certain embodiments, the small molecules are serine-threonine kinase (STK) inhibitors including, but not limited to, dabrafenib (Tafinlar); everolimus (Afinitor); temsirolimus (Torisel); trametinib (Mekinist); and vemurafenib (Zelboraf) that targets the mutant BRAF V660E protein and is approved for treatment of melanoma.

In certain embodiments, the targeted cancer therapy is immunotherapy with monoclonal antibodies (mAbs) that trigger the body's immune system to fight and destroy cancer cells. In certain embodiments, the mAb is a non-conjugated monoclonal antibody that binds to a target antigen on the surface of cancer cells and activates the immune system to attack the cancer cells or to block protein that helps the cancer cells grow and is located within or on surface of tumors or in the tumor microenvironment. Examples of mAbs for cancer therapy include: alemtuzumab (Campath), that binds CD52 antigen found on lymphocytes, and approved for CLL; bevacizumab (Avastin), that binds VEGF and is indicated for treatment of glioblastoma, renal cell carcinoma, and metastatic breast, lung, and colon cancer; cetuximab (Erbitux) that targets EGFR and is indicated for treatment of colon cancer, metastatic colorectal cancer and head and neck cancer; daratumumab (Darzalex) that targets CD38 and is indicated for treatment of multiple myeloma also in combination with bortezomib, melphalan and prednisone (VMP) in early stages of the disease; olaratumab (Lartruvo), an mAb that targets PDGFR-alpha, a protein on cancer cells, and can be used with doxoruhicin to treat soft tissue sarcomas; panitumumab (Vectibix) targets EGFR and is indicated for treatment of metastatic colorectal cancer alone or in combination with FOLFOX chemotherapy; and trastuzumab (Herceptin) that targets HER2 protein and is indicated for treatment of certain breast and stomach cancer.

In certain embodiments, the targeted cancer therapy is immunotherapy with immune checkpoint inhibitors (ICI) including, but not limited to, inhibitors to the checkpoints PD-1 or its known ligand, PD-L1, or CTLA-4 that are found on T cells and other immune cells such as macrophages. The ICI are preferably monoclonal antibodies (mAbs) and include, but are not limited to, anti-CTLA-4 mAbs such as ipilimumab (Yervoy) and tremelimumab; anti-PD-1 mAbs such as nivolumab (Opdivo), pidilizumab, pembrolizumab (Keytruda; formerly called lambrolizumab); and anti-PD-L1 mAbs such as atezolizumab (Crecentrig), avelumab (Bavencio) and durvalumab (Imfinzi). Reference is made to copending International Patent Application No. PCT/IL2018/050609 entitled “Method of predicting personalized response to cancer treatment with immune checkpoint inhibitors and kits therefor” filed by applicant at the Israel PCT Receiving Office (RO/IL) on the same date, the contents thereof being partially included in the present application only for proof of enablement of the method of the invention for predicting the response of a cancer patient to treatment with a cancer therapy for all presently known cancer modalities.

In certain embodiments, the targeted cancer therapy is anti-angiogenic therapy. In certain embodiments, the antiangiogenic drug is a monoclonal antibody that targets VEGF, including the above-mentioned bevacizumab and panitumumab, block VEGF attachment to its receptors and this stops the blood vessels from growing. In certain embodiments, the antiangiogenic drug is a tyrosine-kinase inhibitor such as the above-mentioned sunitinib that stops the VEGF receptors from sending growth signals into the blood vessel cells.

In certain embodiments, the targeted therapy involves conjugated mAbs, also referred to as tagged, labeled or loaded antibodies, in which the mAb is linked to a chemotherapy drug or to a radioactive particle that is delivered directly to the cancer cells while the mAb functions as the homing agent and binds onto the target antigen in the cell. In certain embodiments, the conjugated mAb is a radiolabeled antibody with small radioactive particles attached to it, e.g., ⁹⁰Y-ibritumomab tiuxetan (Zevalin) that targets the CD20 antigen found on B cells and is used to treat some types of non-Hodgkin lymphoma. In certain embodiments, the conjugated mAb is a chemolabeled antibody also called antibody-drug conjugate (ADC), e.g., ado-trastuzumab emtansine or T-DM1 (Kadcyla®) that targets HER2, attached to the DM1 chemo drug, and is used to treat some breast cancer patients whose cancer cells have too much HER2.

In certain embodiments, the targeted cancer therapy is hormonal therapy for slowing or stopping the growth of hormone-sensitive tumors, which require certain hormones to grow, for example, in prostate and breast cancers.

In certain embodiments, the targeted cancer therapy is photodynamic therapy (PDT), more particularly vascular-targeted photodynamic therapy (VTP), recently approved for padeliporfin/WST-11(Tookad) for treatment of localized prostate cancer

The host-driven factors/biomarkers identified by the method of the invention, after administration of a cancer therapy to a cancer patient, are specific to: (i) the cancer patient; and (ii) to the cancer therapy modality. In each modality, the response is specific also to the specific drug or combination of drugs used. In a combination of modalities, the response is specific to the combination of modalities used. This is the “host response” that provides specific information about the reaction of the cancer patient to the treatment and allows the prediction in a personalized form to help diagnose, plan treatment, find out how well treatment is working, or make a prognosis.

If the cancer therapy modality is, for example, chemotherapy with one single drug, the factors generated by the host/patient are specific to this particular drug. If the chemotherapy is carried out with a combination of two or more chemotherapeutic drugs, the factors generated by the host/patient are specific to this combination of the two or more chemotherapeutic drugs.

In certain embodiments, the biomarkers are molecular factors that may be cytokines, chemokines, growth factors, enzymes or soluble receptors. Some of these factors induce cells that affect the tumor and contribute to tumor angiogenesis and cancer re-growth, thereby promoting resistance to the therapy used. Examples of such cells include bone-marrow derived cells (BMDCs) that are mobilized from the bone-marrow compartment by cytokines and growth factors such as G-CSF and SDF-1α, and subsequently colonize the treated tumors and promote cancer therapy resistance, particularly, but not exclusively, chemotherapy resistance. Other cells are immune cells such as macrophages and antigen-presenting cells, or stromal cells within the tumor microenvironment which play a pivotal role in tumor progression.

The host-mediated cellular and molecular mechanisms that contribute to tumor resistance to a cancer therapy are based on the biological functions of the factors and/or cells generated in the host by the particular cancer therapy. Each factor may exhibit one or more biological functions or activities.

In certain embodiments, the factors are pro-tumorigenic and contribute to tumor growth. In certain embodiments, the pro-tumorigenic factors are pro-angiogenic. In other embodiments, the pro-tumorigenic factors are pro-inflammatory/chemotactic. In yet other embodiments, the pro-tumorigenic factors are proliferative growth factors.

In certain embodiments, the pro-angiogenic factors include, without being limited to, ANG (angiogenin); angiopoietin-1; angiopoietin-2; bNGF (basic nerve growth factor); cathepsin S; Galectin-7; GCP-2 (granulocyte chemotactic protein, CXCL6); G-CSF (granulocyte-colony stimulating factor); GM-CSF (granulocyte-macrophage colony stimulating factor, also known as colony-stimulating factor 2, CSF2); PAI-1 (plasminogen activator inhibitor-1); PDGF (platelet-derived growth factor) selected from PDGF-AA, PDGF-BB, PDGF-AB; PlGF (or PLGF, placental growth factor); PlGF-2; SCF (stem-cell factor); SDF-1 (CXCL12, stromal cell-derived factor-1); Tie2 (or TIE-2, an endothelial receptor tyrosine kinase); VEGF (vascular endothelial growth factor) selected from VEGF-A, VEGF-C and VEGF-D; VEGF-R1; VEGF-R2; and VEGF-R3.

In certain embodiments, the pro-inflammatory and/or chemotactic factors include, without being limited to, 6Ckine (CCL21, Exodus-2); angiopoietin-1; angiopoietin-2; BLC (CXCL13, B lymphocyte chemoattractant or B cell-attracting chemokine 1 (BCA-1); BRAK (CXCL14); CD186 (CXCR6); ENA-78 (CXCL5, Epithelial cell derived neutrophil activating peptide 78,); Eotaxin-1 (CCL11); Eotaxin-2 (CCL24); Eotaxin-3 (CCL26); EpCAM (Epithelial cell adhesion molecule); GDF-15 (growth differentiation factor 15, also known as macrophage inhibitory cytokine-1, MIC-1); GM-CSF; GRO (growth-regulated oncogene); HCC-4 (CCL16, human CC chemokine 4); 1-309 (CCL1); IFN-γ; IL-1α; IL-1β; IL-1R4 (ST2); IL-2; IL-2R; IL-3; IL-3Rα; IL-5; IL-6; IL-6R; IL-7; IL-8; IL-8 RB (CXCR2, interleukin 8 receptor, beta); IL-11; IL-12; IL-12p40; IL-12p70; IL-13; IL-13 R1; IL-13R2; IL-15; IL-15Rα; IL-16; IL-17; IL-17C; IL-17E; IL-17F; IL-17R; IL-18; IL-18BPa; IL-18 Ra; IL-20; IL-23; IL-27; IL-28; IL-31; IL-33; IP-10 (CXCL10, interferon gamma-inducible protein 10); I-TAC (CXCL11, Interferon-inducible T-cell alpha chemoattractant); LIF (Leukemia inhibitory factor); LIX (CXCL5, lypopolysaccharide-induced CXC chemokine); LRP6 (low-density lipoprotein (LDL) receptor-related protein-6); MadCAM-1 (mucosal addressin cell adhesion molecule 1); MCP-1 (CCL2, monocyte chemotactic protein 1); MCP-2 (CCL8); MCP-3 (CCL7); MCP-4 (CCL13); M-CSF (macrophage colony-stimulating factor, also known as colony stimulating factor 1 (CSF1); MIF (macrophage migration inhibitory factor); MIG (XCL9, Monokine induced by gamma interferon); MIP-1 gamma (CCL9, macrophage inflammatory protein-1 gamma); MIP-1α (CCL3); MIP-1β; MIP-1δ (CCL15); MIP-3a (CCL20); MIP-3β (CCL19); MPIF-1 (CCL23, Myeloid progenitor inhibitory factor 1); PARC (CCL18, pulmonary and activation-regulated chemokine); PF4 (CXCL4, platelet factor 4); RANTES (CCL5, regulated on activation, normal T cell expressed and secreted); Resistin; SCF; SCYB16 (CXCL16, small inducible cytokine B16); TACI (transmembrane activator and CAML interactor); TARC (CCL17, CC thymus and activation related chemokine); TSLP (Thymic stromal lymphopoietilf). TNF-α (tumor necrosis factor-α); TNF-R1; TRAIL-R4 (TNF-Related Apoptosis-Inducing Ligand Receptor 4); TREM-1 (Triggering Receptor Expressed On Myeloid Cells 1).

In certain embodiments, the proliferative factors include, without being limited to, Activin A; Amphiregulin; Axl (AXL, a receptor tyrosine kinase); BDNF (Brain-derived neurotrophic factor); BMP4 (bone morphogenetic protein 4); cathepsin S; EGF (epidermal growth factor); FGF-1 (fibroblast growth factor 1); FGF-2 (also known as bFGF, basic FGF); FGF-7; FGF-21; Follistatin (FST); Galectin-7; Gas6 (growth arrest-specific gene 6); GDF-15; HB-EGF (heparin-binding EGF); HGF; IGFBP-1 (Insulin-like growth factor binding protein-1); IGFBP-3; LAP (Latency-associated peptide); NGF-R (nerve growth factor receptor); NrCAM (neuronal cell adhesion molecule); NT-3 (neurotrophin-3); NT-4; PAI-1; TGF-α (transforming growth factor-α); TGF-β; and TGF-β3; TRAIL-R4 (TNF-Related Apoptosis-Inducing Ligand Receptor 4).

In certain embodiments, the pro-metastatic factors include, without being limited to, ADAMTS1 (A disintegrin and metalloproteinase with thrombospondin motifs 1); cathepsin S; FGF-2; Follistatin (FST); Galectin-7; GCP-2; GDF-15; IGFBP-6; LIF; MMP-9 (Matrix metallopeptidase 9, also known as 92 kDa gelatinase or gelatinase B (GELB); pro-MMP9; RANK (receptor activator of nuclear factor kB, also known as TRANCE receptor or TNFRSF11A) and its receptor RANKL; RANTES (CCL5); SDF-1 (stromal cell-derived factor 1, also known as CXCL12) and its receptor CXCR4.

The factors may also be anti-tumorigenic factors, e.g., anti-angiogenic, anti-inflammatory and/or anti-proliferative growth factors.

Depending on the cancer therapy modality, the treatment is made in one single session, e.g., surgery, but in most of the modalities such as chemotherapy, radiation therapy, targeted therapy, and immunotherapy, the treatment comprises multiple sessions. In cancer therapy, a cycle of treatment means that the drug is administered to the patient at one point in time (for example, injections over a day or two) and then there is some time of rest (e.g., 1, 2 or 3 weeks) with no treatment. The treatment and rest time make up one treatment cycle. When the patient gets to the end of the cycle, it starts again with the next cycle. A series of cycles of treatment is called a course.

As used herein, “a session of treatment” refers to the “one point in time” when the patient receives the treatment with a drug or another treatment such as radiation at the beginning of a cycle of treatment.

As used herein, the terms “a drug” and “the drug” refer to a single drug, a combination of drugs of the same modality such as two or more chemotherapeutic drugs, or a combination of drugs related to different cancer therapy modalities.

In certain embodiments, the session of treatment is one of multiple sessions of treatment, and the biological sample, preferably blood plasma, is obtained from the cancer patient at about 20, 24 hours or more after said one of multiple sessions of treatment. In certain embodiments, the sample is obtained at 30, 36, 40, 48, 50, 60, 72 hours or more, including up to one to three weeks, after said one of multiple sessions of treatment.

The levels of the plurality of factors generated by the host/cancer patient in response to the treatment with the immune checkpoint inhibitor are determined in the biological sample, preferably blood plasma, obtained from the patient post-treatment. The value (factor concentration in pg/mL) obtained for each factor is then compared with a reference level, which is the baseline level of concentration of the same factor determined in a biological sample, preferably blood plasma, obtained previously from the same cancer patient (hereinafter “reference/baseline sample”).

In certain embodiments of the invention, the one of multiple sessions of treatment of the cancer patient is the first session of treatment, when the treatment with the drug is started. In this case, the reference/baseline sample is obtained from the cancer patient at a time point before starting the treatment with the drug. In this case, the comparison is made between the concentration level of each of the factors determined in the biological sample, preferably blood plasma, obtained from the cancer patient after the first treatment with the drug, and the same factors found in the reference/baseline biological sample, preferably plasma, obtained from the cancer patient at a time point before starting treatment with the drug. In certain embodiments, this time point is at about 72 hours or less, including at about 60, 50, 48, 40, 36, 30, or 24, 20 hours or just before the first session of treatment.

In certain other embodiments of the invention, the one of multiple sessions of treatment is not the first session of treatment. In this case, the biological sample is obtained from the cancer patient at any time point between two consecutive sessions of treatment, wherein said biological sample is simultaneously the biological sample of step (i) and the reference/baseline biological sample according to step (ii) for the next session assay according to step (i). This means that the reference/baseline sample for this session is the same biological sample obtained from the cancer patient at a time point after the session of treatment that preceded said session that is not the first session. The time between two consecutive sessions of treatment may be from one day to 1 or 3 weeks, depending on the cancer therapy, and the biological sample is obtained from the cancer patient at about 20, 24, 30, 36, 40, 48, 50, 60, 72 hours or more, including up to one to three weeks or more, after the session of treatment that is not the first session of treatment with the cancer therapy.

In accordance with the invention, the change in the level of one or more of the factors/biomarkers identified in the biological sample obtained from the cancer patient after the treatment with the drug or another cancer therapy modality compared to the reference/baseline level, is defined by the fold change for each factor. The fold change for each factor is determined by calculating the ratio of treatment:reference/baseline values for the factor.

In certain embodiments, the fold change denotes an increase (up-regulation) of at least 1.5-fold or a decrease (down-regulation) of at least 0.5-fold in the level of each of the one or more of the tumorigenic factors generated by the cancer patient in response to the treatment with the drug or another therapy modality. A fold change of ≥1.5 indicating upregulation of the tumorigenic factor or a fold change of ≤0.5 indicating down-regulation of the tumorigenic factor are considered significant according to the invention and predictive of a non-favorable or a favorable response, respectively, of the cancer patient to the treatment with the drug or another therapy modality.

The change in the level of one or more of the factors/biomarkers identified in the biological sample obtained from the cancer patient after the treatment with a drug or another therapy modality compared to the reference/baseline level, if significant, predicts a favorable or a non-favorable response of said cancer patient to said cancer therapy. The fold change is considered significant if it is of at least about 1.5 fold or higher, i.e., ≥1.5 (up-regulation), or if it is at least about 0.5 fold or lower, i.e., ≤0.5 (down-regulation). As used herein, the fold change is “considered significant” if it is predictive of a favorable or a non-favorable response of the cancer patient to said treatment with the drug or another therapy modality.

The fold change is determined for all circulating factors in the patient's biological sample. The prediction of a favorable or a non-favorable response of the cancer patient to the treatment will be based on significant fold changes of one or more, optionally two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, or fifteen or more, or 20-25 or more of the circulating factors.

In certain embodiments, the change is an increase (up-regulation) of at least about 1.5 fold in the level of one or more of the biomarkers. If the increase is in the level of biomarkers that are pro-tumorigenic, this indicates a non-favorable response of the cancer patient to the treatment.

In certain embodiments, the change is a decrease (down-regulation) of at least about 0.5 fold in the level of one or more of the biomarkers. If the decrease is in the level of biomarkers that are pro-tumorigenic, this indicates a favorable response of the cancer patient to the treatment.

In certain embodiments, the session of treatment is the first session of a plurality of sessions of treatment of the cancer patient, when the treatment is started. In this case, the comparison is between the factors determined in the biological sample, preferably plasma, obtained from the cancer patient after first starting the treatment, and the same factors found in the reference/baseline biological sample, preferably plasma, obtained from the cancer patient before starting treatment. These results may assist the medical oncologists treating the patient to decide if or how to continue the treatment of the cancer patient.

In certain embodiments, the method of the invention is performed for monitoring treatment response in a cancer patient being treated with a cancer therapy. In this case, the session of treatment is one of the sessions of several sessions of treatment, but not the first one. The results will assist the medical oncologist in their decisions if or how to continue the treatment.

In certain embodiments, the fold change determined for pro-tumorigenic factors is predictive of the patient's favorable response to the cancer therapy and the decision may be to continue the same treatment as scheduled.

In certain embodiments, the fold change determined for pro-tumorigenic factors is predictive of the patient's non-favorable response to the treatment. In this case, depending on the specific biological activity of the pro-tumorigenic factors, the decision may be to continue the same treatment with the same drug or modality, but with the addition of a drug that blocks the biological activity of the pro-tumorigenic factors, for example, by adding to the treatment an anti-inflammatory drug if the factors are pro-inflammatory or by adding to the treatment an anti-angiogenic drug if the factors are pro-angiogenic.

In certain embodiments, the fold change determined for pro-tumorigenic factors is predictive of the patient's non-favorable response to the drug and the medical oncologist's decision may be to change the treatment using a different drug, or to use a combination of two or more drugs of the same modality, e.g., two chemotherapeutic drugs, or a combination of the drug with a drug used in another cancer therapy modality, for example, a combination of a chemotherapeutic drug with a drug used in targeted cancer therapy.

In certain embodiments, the cancer therapy is chemotherapy that is typically given in cycles.

In accordance with the invention, chemotherapy is conducted with a single chemotherapy drug (paclitaxel) or with a combination of two drugs (Adriamycin/Cyclophosphamide (AC)) or of three drugs (Folinic acid/Fluorouracil/Oxaliplatin (FOLFOX)).

In certain embodiments, based on Table 3 herein, the circulating factors indicating a host response to chemotherapy include, but are not limited to: 6Ckine; Activin A; Amphiregulin; Angiogenin; Angiopoietin-1; Axl; BDNF; BLC; BMP4; bNGF; Cathepsin S; EGF; ENA-78; Eotaxin; Eotaxin-2; Eotaxin-3; EpCAM; Fcr RIIB/C; FGF-2; FGF-7; Follistatin; Galectin-7; GCP-2; G-CSF; GDF-15; GH; GRO; HB-EGF; HCC-4; I-309; IGFBP-1; IGFBP-6; IL-1α; IL-1β; IL-1ra; IL-2; IL-2 Rb; IL-8; IL-11; IL-12p40; IL-12p70; IL-13 R1; IL-13 R2; IL-16; IL-17; IL-17B; IL-17F; IL-18BPa; IL-23; IL-28A; IP-10; I-TAC; LAP; LIF; Lymphotactin; MCP-1; MCP-2; MCP-3; M-CSF; MDC; MIF; MIG; MIP-1a; MIP-1δ; MIP-3α; MIP-3β; MPIF-1; NGF R; NrCAM; NT-3; NT-4; PAI-1; PARC; PDGF-AA; PDGF-AB; PDGF-BB; PF4; PlGF; PlGF-2; RANTES; Resistin; SCF; SDF-1α; ST2; TARC; TECK; TGFα; TGFβ; TGFβ3; Tie-2; TNFα; TNF R1; TRAIL-R4; TREM-1; TLSP; VEGF; VEGF-D; VEGF-R1; VEGF-R2; VEGF-R3.

In one embodiment in accordance with the present invention, the circulating factors shown in Table 3 that were unregulated indicating a host response to chemotherapy with Adriamycin/Cyclophosphamide (AC) or Folinic acid/Fluorouracil/Oxaliplatin (FOLFOX) include: the pro-angiogenic factors: angiogenin; angiopoietin-1; G-CSF; PDGF-AA; PDGF-AB; PDGF-BB; PlGF; SCF; Tie-2; VEGF A; and VEGF D; the pro-inflammatory and/or chemotactic factors include: BLC (CXCL13); ENA-78 (CXCL5); Eotaxin-3; G-CSF; GDF-15; I-309 (CCL1); IL-1α; IL-1β; IL-1ra; IL-2; IL-8; IL-11; IL-12p40; IL-12p70; IL-13R1; IL-13R2; IL-16; IL-17; IL-17B; IL-17F; IL-18BPa; IL-23; IL-28A; IP-10 (CXCL10); MCP-3; M-CSF; MIF; MIG (CXCL9); MIP-1δ (CCL15); MIP-3α; MIP-3β (CCL19); RANTES (CCL5); SCF; ST2 (IL-1R4); and TARC (CCL17); and the proliferative growth factors include: BDNF; EGF; FGF-7; IGFBP-1; NrCAM; NT-3; NT-4; TGF-α; and TGFβ.

In another embodiment in accordance with the present invention, the circulating factors shown in Table 4 that were unregulated indicating a host response to chemotherapy with paclitaxel or Folinic acid/Fluorouracil/Oxaliplatin (FOLFOX) include: the pro-angiogenic factors SDF-1 and VEGF-C; the pro-inflammatory and/or chemotactic factors CXCL14 (BRAK); CXCL16; CXCR2 (IL-8 RB); CXCR6; GM-CSF; IL-1alpha; IL-1R4 (ST2); IL-3Ralpha; IL-7Ralpha; IL-9R; IL-10; IL-11; IL-12p70; IL-15; IL-15Ralpha; IL-17; IL-17R; IL-18R alpha; IL-20; IL-27; IL-28; IL-31; LIF; LIX; LRP-6; MadCAM-1; MCP-1; M-CSF; MIP-1gamma; MIP-2; TACI; and TARC; the proliferative growth factors IGFBP-1; TGF-beta 1; and TGF-beta 2; and the pro-metastatic factor MMP-9.

In another embodiment, the cancer therapy is targeted therapy with the protease inhibitor bortezomib. The circulating factors shown in Table 6 that were unregulated indicating a host response to therapy with bortezomib include the pro-angiogenic factors PlGF-2 and VEGF-D; the pro-inflammatory and/or chemotactic factors CCL28; IL-1alpha; IL-1R4 (ST2); IL-3; IL-5; IL-6; IL-6R; IL-10; IL-11; IL-12p70; IL-13; IL-17C; IL-17E; IL-31; MCP-1; M-CSF; and MIP-3beta′ and the proliferative growth factors IGFBP-1; IGFBP-3; and TGF-beta 3.

In another embodiment, the cancer therapy is radiation therapy. The circulating factors shown in Table 8 that were upregulated indicating a host response to radiation therapy include the pro-angiogenic factors angiogenin; angiopoietin-1; PDGF-AA; PDGF-BB; PLGF-2; and SDF-1; the pro-inflammatory and/or chemotactic factors IL-10; and MCP-1; and the proliferative growth factors EGF; and FGF-1.

In another embodiment, the cancer therapy is surgery. The circulating factors shown in Table 9 that were upregulated indicating a host response to surgery include the pro-angiogenic factors angiopoietin-1; PDGF-AA; PDGF-BB; and PLGF-2; and the pro-inflammatory and/or chemotactic factor MCP-1.

In another aspect, the present invention provides a kit comprising a plurality of antibodies, at least part of the antibodies of the plurality of antibodies each selectively binding to each of a plurality of factors that promote responsiveness or non-responsiveness of a cancer patient to treatment with a cancer therapy modality, and instructions for use.

In certain embodiments, the kit is any type of antibody array to detect the levels of proteins. In certain embodiments, the kit is a sandwich or enzyme-linked immunosorbent assay (ELISA) that uses solid-phase enzyme immunoassay (EIA) to detect the presence of a substance, usually an antigen, in a liquid sample or wet sample, According to the present invention, this liquid sample is a biological sample obtained from a cancer patient undergoing treatment with a cancer therapy.

In certain embodiments, the kit comprises a plurality of human monoclonal antibodies, at least part of them each binding specifically to a pro-tumorigenic or a pro-metastatic activity factor wherein the pro-tumorigenic factors have pro-angiogenic, pro-inflammatory/chemotactic, or proliferative activity, at least some of these pro-tumorigenic and pro-metastatic factors being predictive of a favorable or a non-favorable response of a cancer patient to treatment with a cancer therapy modality. The kit will of course comprise additional antibodies for binding to potential candidates pro-tumorigenic factors. The number of monoclonal antibodies in the kit will be determined according to the producer's decision.

Thus, in certain embodiments, the cancer therapy is chemotherapy and the kit will comprise monoclonal antibodies that specifically bind to pro-tumorigenic factors identified according to the method of the invention to be generated by the host in response to chemotherapy. The kit will of course comprise additional antibodies for binding to potential candidate pro-tumorigenic factors.

In certain preferred embodiments, the kit is for use according to the present invention.

In another aspect, the present invention is directed to a method of treatment of a cancer patient with a cancer therapy, the method comprising the steps of:

-   -   (i) performing an assay on a biological sample selected from         blood plasma, whole blood, blood serum or peripheral blood         mononuclear cells obtained from the cancer patient at a time         period after a session of treatment with said cancer therapy, to         determine the levels of one or more of a plurality of factors         induced in the circulation of said cancer patient in response to         treatment with said cancer therapy, said one or more of the         plurality of factors promoting responsiveness or         non-responsiveness of the cancer patient to the treatment with         said cancer therapy;     -   (ii) obtaining reference levels for each of the one or more of         the plurality of the induced factors of step (i) in a biological         sample selected from blood plasma, whole blood, blood serum or         peripheral blood mononuclear cells, obtained from the cancer         patient before said session of treatment with the cancer         therapy;     -   (iii) establishing the fold change for each of the one or more         of the plurality of the induced factors of step (i) by comparing         the level of each induced factor of step (i) with the reference         level of step (ii) for the same factor;     -   (iv) determining that the cancer patient has a favorable or a         non-favorable response to the treatment with said cancer therapy         based on the fold change established in step (iii) for one or         more of the plurality of induced factors of step (i); and     -   (iva) if the cancer patient has a non-favorable response to the         treatment with said cancer therapy based on the fold change         established in (iii) for one or more of the plurality of the         induced factors, then selecting a dominant factor among the one         or more factors showing a fold change indicative of said         non-favorable response, and treating the patient with the cancer         therapy in combination with an agent that blocks the dominant         factor; or     -   (ivb) if the cancer patient has a favorable response to the         treatment with said cancer therapy based on the fold change of         the level of the one or more factors established in (iii), then         continuing the treatment of the cancer patient with the same         cancer therapy.

In preferred embodiments of the invention, both biological samples of steps (i) and (ii) are blood plasma from the cancer patient.

In certain embodiments, the session of treatment of the cancer patient with the cancer therapy is the first session of treatment with said cancer therapy, the biological sample of step (i) is obtained from the cancer patient at about 20, 24, 30, 36, 40, 48, 50, 60, 72 hours or more, including up to one to three weeks or more, after said first session of treatment, depending on the cancer therapy modality, and the reference biological sample of step (ii) is obtained from the cancer patient at a time point including at about 72 hours or less, including at about 60, 50, 48, 40, 36, 30, 24 or 20 hours or just before said first session of treatment with the cancer therapy.

In certain embodiments, the session of treatment of the cancer patient with the cancer therapy is one of multiple sessions of treatment that is not the first session of treatment with the cancer therapy, and the biological sample is obtained from the cancer patient at any time point between two consecutive sessions of treatment, wherein said biological sample is simultaneously the biological sample of step (i) and the reference biological sample according to step (ii) for the next session assay according to step (i).

Depending on the cancer therapy modality and the treatment protocol, the time between two consecutive sessions of treatment is from one day to 1 or 3 weeks, and the biological sample is obtained from the cancer patient at about 20, 24, 30, 36, 40, 48, 50, 60, 72 hours or more, including up to one to three weeks or more, after the session of treatment that is not the first session of treatment with the cancer therapy. For example, a regular protocol of radiotherapy treatment comprises sessions of 5 times per week in a schedule of 3 to 9, preferably 5-8, weeks, and the biological sample may be obtained at about 20 to 24 hours between two consecutive sessions of treatment. Chemotherapy with Doxorubicin/Cyclophosphamide (AC) or with Paclitaxel/Doxorubicin/Cyclophosphamide (TAC) is carried out in 4 to 6 cycles with intervals of 14-20 days between the cycles, and the biological sample may be obtained close to about 2-3 weeks between two consecutive sessions of treatment, i.e., just before the next session. Immunotherapy with monoclonal antibodies, e.g., trastuzumab (Herceptin) is carried out with weekly administrations, and the biological sample may be obtained close to about 1 week between two consecutive sessions of treatment, i.e., just before the next session.

The fold-change established in step (iii) is defined by a fold change of ≥1.5 indicating upregulation or a fold change of ≤0.5 indicating down-regulation in the level of each of the one or more of the plurality of factors induced in the circulation of the cancer patient in response to the treatment with the cancer therapy, these values being considered significant and predictive of a non-favorable or favorable response of the cancer patient to the treatment with said cancer therapy. The prediction of a favorable or a non-favorable response of the cancer patient to the treatment with the cancer therapy is based on significant fold changes of one or more, optionally two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, or twenty to twenty five or more, of the induced factors.

The factors induced in the circulation of the cancer patient in response to treatment with said cancer therapy are molecular factors including cytokines, chemokines, growth factors, enzymes and soluble receptors. The factors may be pro-tumorigenic or pro-metastatic factors, and the pro-tumorigenic factors may be pro-angiogenic, pro-inflammatory/chemotactic or proliferative growth factors.

In accordance with the invention, if there is an increase (up-regulation) of at least about 1.5-fold in the level of one or more of the pro-tumorigenic factors or of the pro-metastatic factors, then the prediction is of a non-favorable response of the cancer patient to the treatment with the cancer therapy, and if there is a decrease (down-regulation) of at least about 0.5-fold in the level of one or more of the pro-tumorigenic factors or of the pro-metastatic factor, then the prediction is of a favorable response of the cancer patient to the treatment with the cancer therapy.

According to the method of the invention for treating a cancer patient with a cancer therapy, if the cancer patient has a non-favorable response to the treatment with said cancer therapy based on the fold change established in (iii) for one or more of the plurality of the induced factors, a selection of a dominant factor is made among the one or more factors showing a fold change indicative of said non-favorable response, and the patient is treated with the same cancer therapy in combination with an agent that blocks the dominant factor.

The terms “block”, “neutralize” or “inhibit” are herein used interchangeably and refer to the capability of an agent of preventing the factor from exerting its function/biological activity.re

As used herein, the term “dominant factor” denotes a potent factor that may be upstream of a signaling pathway that affects a biological process that is vital for the living cell and living organism. These biological processes include proliferation, inflammation, metastasis, and others, and are made of several signaling pathways ultimately leading to activation or inhibition of the biological process. A “signaling pathway” is a row of events in which proteins in the same pathway transfer signal to each other. After the first protein in a pathway receives a signal, it activates another protein which activates another protein and so forth, ultimately leading to activation of one or more cell functions.

A “dominant factor” may also be a key factor that highly interacts with, and highly affects, many other factors/proteins. According to the invention, the dominant factors are selected based on an algorithm which identifies the protein-protein interactions of factors based on the literature. When a factor has more interactions, it serves as a hub and therefore it is a dominant factor. The term “protein-protein interactions” refers to physical interactions or cross-talk between two or more proteins, resulting in activation or inhibition of signal transduction or protein activity. The term “protein hubs” refers to highly connected proteins that play central and essential role in biological processes and thus may confer the host with resistance, limit or counteract the effectiveness of the treatment of the cancer patient with the cancer therapy modality.

Examples of dominant factors include, without limitation, EGF, EGFR, FGF, IFN-γ, IL-1β, IL-2, IL-6, PDGF, TNF-α and VEGF-A. All these factors and other dominant factors appear in the tables of the present application as host response to one or more cancer therapy modalities and are all part of the present invention.

To illustrate their qualifications as dominant factors, the properties of some of these factors is provided herein. Interleukin-1β (IL-1β, IL-1b) is a cytokine member of the IL-1 family, produced by different immune cells including macrophages. It is a potent mediator of the inflammatory response and also known to be involved in several biological processes such as cell proliferation and apoptosis, as well as cell differentiation. IL-1b was mostly investigated as a protein that initiates the pro-inflammatory cascade. It physically interacts with enzymes such as CASP1, IL1RA, IL1R1, CMA1, IL1RB, IL1A, IL1R2; genetically interacts with MAPK8IP2, ZNF675 and UBEN2N; and is co-expressed with A2M, CXCL8, IL18, CAASp1, IL1R1 and others. Thus, IL-1b serves as a hub for interactions with a large number of proteins that affect several biological pathways including cell proliferation, apoptosis and differentiation as well as inflammation and angiogenesis.

Another dominant factor is Interleukin-6 (IL-6), which is a cytokine that acts mainly as a pro-inflammatory factor but also sometimes as an anti-inflammatory factor produced by muscle cells and as a result downregulate a number of pro-inflammatory proteins such as IL-1, IL-10 and TNF-α. IL-6 is involved in a number of biological processes including bone formation, disruption of blood brain barrier, macrophage activation and innate immune system contribution, stimulates the synthesis of neutrophils and B cells, and is also involved in neurological activities such as disorders, stress and depression. IL6 interacts and affects a large number of proteins: it physically interacts with HRH1, OSM, IL6ST, IL6R and ZBTB16, and was found to be co-expressed with a large number of proteins such as PTPRE, CSF3, CCL2, CXCL8, CXCL3, ICAM1 SELE, NFKBIZ among others. IL6 is involved in a number of pathways mediated by proteins such as LRPPRC, OSM, PTPRE, PIAS1 and IL6R. As such, IL6 serves as a dominant factor for a number of biological processes involved in immune cell activity, cell genesis, and cell-cell interactions.

A further dominant factor, vascular endothelial growth factor A (VEGF-A), is a growth factor that stimulates the formation of new blood vessels. It is involved in both angiogenesis (endothelial cell proliferation) as well as vasculogenesis (bone marrow-derived endothelial cell precursors and their differentiation). VEGF is important for embryonic cell development and neuronal development in the fetus, and is involved in leukocyte proliferation and differentiation, inflammation and several diseases such as age-related macular degeneration and the majority of cancers. VEGF-A physically interacts with a large number of proteins such as NRP1, NRP2, KDR, FLT1, PGF, THBS1, SPARC, GCP1 and VEGFC; it is co-expressed with SEMA3F, SHB, THBS1, FLT1 and VEGFC; it is involved with proteins of various pathways including PGF, CD2AP, IQGAP1, NEDD4; and it affects a number of biological processes such as angiogenesis, tumorigenesis, cell viability, proliferation and differentiation. As such, VEGF-A is considered a dominant factor, and vital factor for various biological processes both in normal physiological conditions as well as in disease states.

In certain embodiments, the selected dominant factor shows a fold change of ≥1.5 indicative of a non-favorable response of the cancer patient to the treatment with the cancer therapy, and the treatment of the patient with said cancer therapy proceeds in combination with an agent that blocks said dominant factor or the receptor thereof.

The blockade or inhibition of the dominant factor can be done in different ways and by different inhibitors or blocking agents. In certain embodiments, the factor is a cytokine or a growth factor that exerts its biological activity by binding to membrane receptors of target cells, and the blocking agent is an anti-factor monoclonal antibody (mAb) which combines with the factor and thus prevent it from binding to its receptor and thus its capability of exerting is biological function. In this context, the term “neutralizing” the factor is also used. The monoclonal antibodies can be human or humanized monoclonal antibodies, a functional fragment thereof, a monobody or a conjugated antibody. Examples are Infliximab and Adalimumab, humanized mAbs directed against TNF-α.

In certain embodiments, the agent that blocks the factor is a mAb which combines with the factor's receptor, thus preventing the factor's binding to the receptor. Examples are the anti-IL-2R mAbs Basiliximab and Daclizumab.

In certain embodiments, the agent that blocks the factor is a decoy receptor which is a receptor that is able to recognize and bind specific growth factors or cytokines efficiently, but is not structurally able to signal or activate the intended receptor complex. It acts as an inhibitor, binding a ligand and keeping it from binding to its regular receptor. Examples of decoy receptors are 11-1R2, that binds IL-1α and IL-1β, and inhibits their binding to IL-R1; VEGFR-I that inhibits the activity of VEGF-VEGFR-2 axis by sequestering VEGF, thus preventing VEGFR-2 from binding to VEGF and activate VEGF signaling; the drug Etanercept (trade name Enbrel), a fusion protein comprising the sequence of the soluble TNF-R2, which is a receptor that also binds to TNF-α, and inhibits TNF-α of binding to TNF-R1.

In certain embodiments, the dominant factor is selected from factors including EGF, EGFR, FGF, IFN-γ, IL-1β, IL-2, IL-6, PDGF, TNF-α and VEGF-A.

In certain embodiments, the dominant factor is IL-1β, the cancer therapy is chemotherapy, and the cancer patient is treated with chemotherapy in combination with an agent that blocks the activity of IL-1β or blocks its receptor IL-1R, said agent including: (a) an IL-1 receptor antagonist (IL-1Ra), e.g. Anakinra, a recombinant form of the physiologic human protein IL-1Ra which binds the IL-1 type 1 receptor (IL-1R) without causing signaling and thereby prevents activation by the agonistic ligands IL-1α and IL-1β; (b) a soluble decoy IL-1 type II receptor, e.g., Rilonacept; (c) an anti-IL-1β mAb, e.g., Canakinumab, Gevokizumab, LY2189102 or Lutikizumab; (d) an anti-IL-1R mAb, e.g., MEDI-8968 or GSK1827771; (e) an IL-1β-converting enzyme (ICE) inhibitor, e.g., Pralnacasan or Belnacasan; and (f) an IL-1β vaccine.

In certain embodiments, the dominant factor is IL-6, the cancer therapy is chemotherapy, and the cancer patient is treated with chemotherapy in combination with: (a) an agent that blocks the activity of IL-6, said agent including a human or humanized mAb, e.g., Siltuximab, Clazakizumab, Olokizumab, Elsilimomab, or Sirukumab; or (b) an agent that blocks the receptor IL-6R, said agent including a human or humanized monoclonal antibody, e.g., Tocilizumab, Sarilumab, or a nanobody, e.g., Vobarilizumab.

In certain embodiments, the dominant factor is VEGF-A, and the agent that blocks the factor is bevacizumab (Avastin), a humanized mAb. In other embodiments, the factor is EGFR and the agent that blocks the receptor is Cetuximab (trade name Erbitux) or Panitumumab.

The invention will now be illustrated by the following non-limiting Examples.

EXAMPLES Materials

All BALB/c mice used in several examples and the SCID mice of Example 6 were acquired from Harlan, Israel. The rat anti-mouse IgG antibody of Example 6 was from BioxCell, Lebanon, N.H., US.

Example 1. The Effect of Chemotherapy on Circulating Pro-Tumorigenic Factors—a Protein Profiling Approach in Humans

The aim is to define a profile of circulating factors indicative of a pro-tumorigenic host response to chemotherapy in human cancer patients.

A total of 16 breast and 19 colorectal cancer patients were recruited to this study. All breast cancer patients received Adriamycin/Cyclophosphamide (AC) chemotherapy, and all colorectal cancer patients received Folinic acid/Fluorouracil/Oxaliplatin (FOLFOX) chemotherapy according to standard regimens at HaEmek Medical Center, Afula, Israel. Blood samples were drawn (into EDTA tubes) from the patients at 2 time points: i) before receiving the first dose of chemotherapy (baseline); ii) 24 hours after receiving the first dose of chemotherapy (post-treatment). Plasma samples were prepared from whole blood by centrifugation at 1300 g for 10 min at room temperature. Supernatants (representing the plasma samples) were collected and stored at −80° C. until further use. Baseline and post-treatment samples (100 μl) were applied to 4 glass slide-based antibody arrays (RayBiotech; Human Cytokine Array GS2000 and GS4000) according to the manufacturer's instruction. A total of 160 factors were included in the screen, with each array detecting 40 non-overlapping factors. The antibody arrays used, and their respective list of cytokines, enzymes and growth factors, are shown in Table 1 hereinafter. Similar to a traditional sandwich-based array, the glass slide-based arrays make use of a matched pair of specific antibodies to detect cytokines, chemokines, enzymes or growth factors in the plasma sample. The fluorescent readout was detected by a laser fluorescent scanner. Raw data was processed and normalized using the software provided by the manufacturer. Normalized data was then analyzed to identify factors whose circulating levels were changed 24 hours after chemotherapy administration. Specifically, the fold change was determined for each factor by calculating the ratio of post-treatment:baseline values. Candidate factors were chosen based on defined thresholds of fold change. Factors exhibiting a fold change of more than 1.5 or less than 0.5 were defined as being up- or down-regulated, respectively, in response to chemotherapy. The average fold change for up- and down-regulated factors was calculated and is shown in Table 2. Many of these factors are key players in pro-tumorigenic and pro-metastatic processes such as angiogenesis, inflammation, chemotaxis and proliferation. Importantly, each patient exhibited a unique profile of factors. A list of factors found to be up- or down-regulated in response to either chemotherapy type in more than 18% of patients is shown in Table 3.

The upregulated pro-angiogenic factors in Table 3 include: angiogenin; angiopoietin-1; G-CSF; PDGF-AA; PDGF-AB; PDGF-BB; PlGF; SCF; Tie-2; VEGF A; and VEGF D. The up-regulated pro-inflammatory and/or chemotactic factors include: BLC (CXCL13); ENA-78 (CXCL5); Eotaxin-3; G-CSF; GDF-15; I-309 (CCL1); IL-1α; IL-1β; IL-1ra; IL-2; IL-8; IL-11; IL-12p40; IL-12p70; IL-13R1; IL-13R2; IL-16; IL-17; IL-17B; IL-17F; IL-18BPa; IL-23; IL-28A; IP-10 (CXCL10); MCP-3; M-CSF; MIF; MIG (CXCL9); MIP-1δ (CCL15); MIP-3α; MIP-3β (CCL19); RANTES (CCL5); SCF; ST2 (IL-1R4); and TARC (CCL17). The upregulated proliferative growth factors include: BDNF; EGF; FGF-7; IGFBP-1; NrCAM; NT-3; NT-4; TGF-α; and TGFβ.

Example 2. The Effect of Chemotherapy on Circulating Host-Derived Pro-Tumorigenic Factors—a Protein Profiling Approach in Mice

To identify host-derived circulating factors whose levels change in response to chemotherapy, we performed protein array-based screens using plasma from naïve (non-tumor bearing) mice that were treated with different chemotherapy types. The use of naïve mice allows us to identify factors specifically generated by the host in response to chemotherapy, independent of tumor presence. To this end, naïve (non-tumor bearing) 8-10 week old female BALB/c mice (n=5) were treated with either FOLFOX (14 mg/kg oxaliplatin (Medac Pharma, Chicago, Ill., US); 50 mg/kg 5-fluorouracil (Ebewe Pharma, Vienna, Austria); 30 mg/kg folinic acid/leucovorin (ABIC, Israel)) or paclitaxel (BioAvenir Ltd., Israel; 25 mg/kg) chemotherapy administered as a single bolus intraperitoneal injection. Control mice (n=5) were injected with vehicle control. Twenty-four hours after treatment administration, mice were sacrificed, and blood was collected into EDTA-coated tubes by cardiac puncture. Plasma was isolated by centrifugation of whole blood at 1300 g for 10 minutes at room temperature. Supernatants (representing the plasma samples) were collected and pooled per group. Aliquots were stored at −80° C. until further use. Control and treatment plasma samples were applied to a glass slide-based Mouse L308 Array (RayBiotech; Cat no: AAM-BLG-1-2) according to the manufacturer's instruction to screen a total of 308 factors. The full list of cytokines, enzymes and growth factors detected by the array is shown in Table 4. The fluorescent readout was detected by a laser fluorescent scanner. Normalized data was analyzed to identify factors whose circulating levels were changed in response to the two chemotherapy types. Specifically, the fold change was determined for each factor by calculating the ratio of treated:control values. Factors exhibiting a fold change of more than 1.5 or less than 0.5 were defined as being up- or down-regulated, respectively, in response to therapy. These factors, and their respective fold changes in response to each chemotherapy type (Paclitaxel, FOLFOX), are listed in Table 5. The data demonstrate that FOLFOX and paclitaxel chemotherapies induce different profiles of up- and down-regulated factors. Many of the factors that were upregulated in response to the chemotherapies are key players in pro-tumorigenic and pro-metastatic processes such as angiogenesis, inflammation, chemotaxis and proliferation. Upregulated pro-angiogenic factors include: SDF-1 and VEGF-C. Up-regulated pro-inflammatory and/or chemotactic factors include: CXCL14 (BRAK); CXCL16; CXCR2 (IL-8 RB); CXCR6; GM-CSF; IL-1alpha; IL-1R4 (ST2); IL-3Ralpha; IL-7Ralpha; IL-9R; IL-10; IL-11; IL-12p70; IL-15; IL-15Ralpha; IL-17; IL-17R; IL-18R alpha; IL-20; IL-27; IL-28; IL-31; LIF; LIX; LRP-6; MadCAM-1; MCP-1; M-CSF; MIP-1gamma; MIP-2; TACI; and TARC. Upregulated proliferative growth factors include: IGFBP-1; TGF-beta 1; and TGF-beta 2. Upregulated pro-metastatic factors include: MMP-9.

Example 3. The Effect of Bortezomib on Circulating Host-Derived Pro-Tumorigenic Factors—a Protein Profiling Approach in Mice

The molecularly targeted drug, bortezomib (Velcade), is a proteasome inhibitor used for the treatment of multiple myeloma and mantle cell lymphoma. To identify host-derived circulating factors whose levels change in response to bortezomib, we performed a protein array-based screen using plasma from naïve (non-tumor bearing) mice that were treated with bortezomib. The use of naïve mice allows us to identify factors specifically generated by the host in response to bortezomib, independent of tumor presence.

Naïve (non-tumor bearing) 8-10 week old female BALB/c mice (n=5) were intravenously injected with 1 mg/kg bortezomib (Selleckchem, Houston, Tex., US). Control mice (n=5) were injected with vehicle control. Twenty-four hours after treatment administration, mice were sacrificed, and blood was collected into EDTA-coated tubes by cardiac puncture. Plasma was isolated by centrifugation of whole blood at 1300 g for 10 minutes at room temperature. Supernatants (representing the plasma samples) were collected and pooled per group. Aliquots were stored at −80° C. until further use. Control and treatment plasma samples were applied to a glass slide-based Mouse L308 Array (RayBiotech; Cat no: AAM-BLG-1-2), the same array used in Example 2, according to the manufacturer's instruction to screen a total of 308 factors (see Table 4). The fluorescent readout was detected by a laser fluorescent scanner. Normalized data was analyzed to identify factors whose circulating levels were changed in response to bortezomib treatment. Specifically, the fold change was determined for each factor by calculating the ratio of treated:control values. Factors exhibiting a fold change of more than 1.5 or less than 0.5 were defined as being up- or down-regulated, respectively, in response to bortezomib treatment. These factors and their respective fold changes are listed in Table 6. Many of the factors that were upregulated in response to bortezomib are key players in pro-tumorigenic and pro-metastatic processes such as angiogenesis, inflammation, chemotaxis and proliferation. Upregulated pro-angiogenic factors include: PlGF-2 and VEGF-D. Up-regulated pro-inflammatory and/or chemotactic factors include: CCL28; IL-1alpha; IL-1R4 (ST2); IL-3; IL-5; IL-6; IL-6R; IL-10; IL-11; IL-12p70; IL-13; IL-17C; IL-17E; IL-31; MCP-1; M-CSF; and MIP-3beta. Upregulated proliferative growth factors include: IGFBP-1; IGFBP-3; and TGF-beta 3.

Example 4. The Effect of Radiotherapy on Circulating Host-Derived Pro-Tumorigenic Factors—a Protein Profiling Approach in Mice

To identify host-derived circulating factors whose levels change in response to radiotherapy, we performed a protein array-based screen using plasma from naïve (non-tumor bearing) irradiated mice. The use of naïve mice allows us to identify factors specifically generated by the host in response to radiotherapy, independent of tumor presence.

Naïve (non-tumor bearing) 8-10 week old female BALB/c mice (n=5) were locally irradiated to the abdominal cavity with a linear accelerator 6 MeV electron beam using Elekta Precise (ElektaOncology Systems) at a dose rate of 40 cGy per minute, for a total dose of 2 Gy at room temperature. Control mice (n=5) were not irradiated. Twenty-four hours after radiation, mice were sacrificed, and blood was collected into EDTA-coated tubes by cardiac puncture. Plasma was isolated by centrifugation of whole blood at 1300 g for 10 minutes at room temperature. Supernatants (representing the plasma samples) were collected and pooled per group. Aliquots were stored at −80° C. until further use. Control and treatment plasma samples were applied to a membrane-based Proteome Profiler Mouse Angiogenesis Array (R&D Systems; Cat no: ARY015) to screen a total of 53 factors. A full list of cytokines, enzymes and growth factors detected by the array is shown in Table 7. Pixel densities on developed X-ray films were analyzed using transmission mode densitometer and image analysis software. Normalized data was analyzed to identify factors whose circulating levels were changed in response to radiation. Specifically, the fold change was determined for each factor by calculating the ratio of treated:control values. Factors exhibiting a fold change of more than 1.5 or less than 0.5 were defined as being up- or down-regulated, respectively, in response to radiation. These factors and their respective fold changes are listed in Table 8. Many of the factors that were upregulated in response to radiotherapy are key players in pro-tumorigenic and pro-metastatic processes such as angiogenesis, inflammation, chemotaxis and proliferation. Upregulated pro-angiogenic factors include: angiogenin; angiopoietin-1; PDGF-AA; PDGF-BB; PLGF-2; and SDF-1. Up-regulated pro-inflammatory and/or chemotactic factors include: IL-10; and MCP-1. Upregulated proliferative growth factors include: EGF; and FGF-1.

Example 5. The Effect of Surgery on Circulating Host-Derived Pro-Tumorigenic Factors—a Protein Profiling Approach in Mice

To identify host-derived circulating factors whose levels change in response to surgery, we performed a protein array-based screen using plasma from naïve (non-tumor bearing) mice that underwent a surgical procedure. The use of naïve mice allows us to identify factors specifically generated by the host in response to surgery, independent of tumor presence.

Naïve (non-tumor bearing) 8-10 week old female BALB/c mice (n=5) underwent a surgical procedure. Specifically, a 1 cm incision in the abdominal region of mice was made, followed by suturing. Control mice were not operated (n=5 mice/group). Twenty-four hours after the surgical procedure, mice were sacrificed, and blood was collected into EDTA-coated tubes by cardiac puncture. Plasma was isolated by centrifugation of whole blood at 1300 g for 10 minutes at room temperature. Supernatants (representing the plasma samples) were collected and pooled per group. Aliquots were stored at −80° C. until further use. Control and post-surgery plasma samples were applied to a membrane-based Proteome Profiler Mouse Angiogenesis Array (R&D Systems; Cat no: ARY015), the same array used in Example 4 (see Table 7) to screen a total of 53 factors. Pixel densities on developed X-ray films were analyzed using transmission mode densitometer and image analysis software. Normalized data was analyzed to identify factors whose circulating levels were changed in response to surgery. Specifically, the fold change was determined for each factor by calculating the ratio of post-surgery:control values. Factors exhibiting a fold change of more than 1.5 or less than 0.5 were defined as being up- or down-regulated, respectively, in response to surgery. These factors and their respective fold changes are listed in Table 9. Many of the factors that were upregulated after surgery are key players in pro-tumorigenic and pro-metastatic processes such as angiogenesis, inflammation, and chemotaxis. Upregulated pro-angiogenic factors include: angiopoietin-1; PDGF-AA; PDGF-BB; and PLGF-2. Up-regulated pro-inflammatory and/or chemotactic factors include: MCP-1.

Example 6. The Effect of Immune Checkpoint Inhibitor Therapy on Circulating Host-Derived Pro-Tumorigenic Factors—a Protein Profiling Approach in Mice

To identify host-derived circulating factors whose levels change in response to immune checkpoint inhibitor therapy, we performed 3 protein array-based screens using naïve (non-tumor bearing) mice. The use of naïve mice allows us to identify factors specifically generated by the host in response to therapy, independent of the tumor.

In the first screen, naïve 8-10 week old female BALB/c mice (n=3) were intraperitoneally injected with anti-PD-1 rat anti-mouse antibody (BioXCell, West Lebanon, N.H., USA) at a dose of 200 μg/20 gr mouse every other two days over a period of 1 week (3 injections in total). Control mice (n=3) were similarly injected with a rat-anti-mouse IgG antibody at the same dose. One week after the first injection, mice were sacrificed, and blood was collected into EDTA-coated tubes by cardiac puncture. Plasma was isolated by centrifugation of whole blood at 1300 g for 10 minutes at room temperature. Supernatants (representing the plasma samples) were collected and pooled per group. Aliquots were stored at −80° C. until further use. Plasma samples were applied to a membrane-based Proteome Profiler Mouse XL Cytokine Array (R&D Systems; Cat no: ARY028) to screen a total of 111 factors. A full list of cytokines, enzymes and growth factors detected by the array is shown in Table 10. Pixel densities on developed X-ray films were analyzed using transmission mode densitometer and image analysis software. Normalized data was analyzed to identify factors whose circulating levels were changed in response to anti-PD-1 therapy. Specifically, the fold change was determined for each factor by calculating the ratio of treatment:control values. Factors exhibiting a fold change of more than 1.5 or less than 0.5 were defined as being up- or down-regulated, respectively, in response to anti-PD-1 therapy. These factors and their respective fold changes are listed in Table 11. Many of the factors that were upregulated in response to anti-PD-1 therapy are key players in pro-tumorigenic and pro-metastatic processes such as angiogenesis, inflammation, chemotaxis and proliferation. Upregulated pro-angiogenic factors include: G-CSF; GM-CSF; and PDGF-BB. Up-regulated pro-inflammatory and/or chemotactic factors include: CCL17/TARC; CCL5/RANTES; G-CSF; GM-CSF; IFN-gamma; IL-1Ralpha; IL-2; IL-6; IL-7; IL-10; IL-12p40; IL-13; IL-33; and M-CSF. Upregulated proliferative growth factors include: FGF-21; Gas6; and HGF. Upregulated pro-metastatic factors include: MMP-9.

In the second screen, naïve 8-10 week old female BALB/c, male BALB/c, female C57Bl/6 or male C57Bl/6 mice (n=7 mice per group) were intra-peritoneally injected with anti-PD-L1 or control IgG antibodies (BioXCell, West Lebanon, N.H., USA) every other day over a period of 1 week (3 injections in total) at a dose of 200 μg/20 gr mouse per injection. Twenty-four hours after the last administration, mice were sacrificed, blood was drawn and plasma was prepared. Plasma samples from each group were pooled and applied to a glass slide-based Quantibody Mouse Cytokine Array (RayBiotech, Cat no: QAM-CAA-4000) according to the manufacturer's instruction to screen a total of 200 factors. A full list of cytokines, enzymes and growth factors detected by the array is shown in Table 12. The fold changes were determined for each factor on the protein array by calculating the ratio of treated:control values. Factors exhibiting a fold change of more than 1.5 or less than 0.5 were defined as being up- or down-regulated, respectively, in response to anti-PD-L1 therapy. These factors and their respective fold changes are listed in Table 13. The data demonstrate that the profiles of up- and down-regulated factors do not completely overlap when comparing between the different mouse strains or when comparing between males and females of the same strain. This suggests that the response to anti-PD-L1 therapy is genotype-dependent, and can therefore be tested in a personalized manner. Many of the factors that were upregulated in response to anti-PD-L1 therapy are key players in pro-tumorigenic and pro-metastatic processes such as inflammation, chemotaxis and proliferation. Upregulated pro-angiogenic factors include: G-CSF; and SCF. Upregulated pro-inflammatory and/or chemotactic factors include: Eotaxin-2; G-CSF; IL-1ra; IL-6; IL-7; IL-33; I-TAC; MadCAM-1; MCP-5; SCF; and TACI. Upregulated proliferative growth factors include: amphiregulin; Axl; EGF; and HGF. Upregulated pro-metastatic factors include: ADAMTS1 and pro-MMP9.

To gain insight into which host cell types secrete these pro-tumorigenic factors, we performed a similar screen, comparing between BALB/c and SCID mice treated with anti-PD-1 or control IgG antibodies. SCID mice carry the severe combined immune deficiency (SCID) mutation on the BALB/c background, and therefore lack functional adaptive immune cell types (B cells and T cells). Naïve 8-10 week old female BALB/c or SCID mice (n=7 mice per group) were intraperitoneally injected with anti-PD-1 or control IgG antibodies (BioXCell, West Lebanon, N.H., USA) every other day over a period of 1 week (3 injections in total) at a dose of 200 μg/20 gr mouse per injection. Twenty-four hours after the last administration, mice were sacrificed, blood was drawn and plasma was prepared. Plasma samples from each group were pooled and applied to a glass slide-based Quantibody Mouse Cytokine Array (RayBiotech, Cat no: QAM-CAA-4000), the same array used in the second screen above (see Table 12), according to the manufacturer's instruction to screen a total of 200 factors. The fold changes were determined for each factor on the protein array by calculating the ratio of treated:control values. Factors exhibiting a fold change of more than 1.5 or less than 0.5 were defined as being up- or down-regulated, respectively, in response to anti-PD-1 therapy. These factors and their respective fold changes are listed in Table 14. Several factors were found to be up-regulated in response to anti-PD-1 therapy, some of which were specific to BALB/c and not SCID mice, e.g., ADAMTS1; amphiregulin, I-TAC and SCF. These results suggest that these specific factors are secreted by cells of the adaptive immune system in response to anti-PD-1 therapy.

Example 7. The Host Response Score has Predictive Value for Patient Outcome

A total of 17 colorectal cancer patients (from the 19 colorectal cancer patients of Example 1) were enrolled in this study. Patients' characteristics are summarized in Table 15. All patients received Folinic acid/Fluorouracil/Oxaliplatin (FOLFOX) chemotherapy according to standard regimens at HaEmek Medical Center, Afula, Israel. Blood samples were drawn (into EDTA-coated tubes) from the patients at 2 time points: i) immediately before receiving a dose of chemotherapy (pre-treatment); ii) 24 hours after receiving the dose of chemotherapy (post-treatment). Plasma samples were prepared from whole blood by centrifugation at 1300 g for 10 min at room temperature. The plasma levels of 160 factors were analyzed by means of antibody arrays (RayBiotech; Human Cytokine Array GS2000 and GS4000). The fold change was determined for each factor by calculating the ratio of post-treatment:pre-treatment values (see Tables 2 and 3). A host response (HR) score was calculated based on a linear function of fold changes of the factors according to:

${\overset{n}{\sum\limits_{1}}\left( {{{If}\left( {{{FC}(n)} > {{Tup}(n)}} \right)}*{W(n)}*{{FC}(n)}} \right)} + \left( {{{IF}\left( {{{FC}(n)} < {{Tdown}(n)}} \right)}*{W(n)}*\frac{1}{{FC}(n)}} \right)$

wherein

(n)=the number of a factor (Factor 1 . . . Factor n, an acceptable format in mathematical equations that describe a series);

FC=fold change of factor n;

T=threshold

W=weight of factor n (the factor's potency—more potent factors have higher weights);

If—means if the fold change of factor (n) passes the defined threshold (T), then it is considered in the calculation; if not, it is omitted. According to the invention, we defined Tup to be 1.5 and T down to be 0.5

FC designates the fold change of factor (n), based on the value of the factor after treatment divided by the value of the factor before treatment. Out of the relevant factors, we take into account only factors that their expression levels are within the threshold (T). The threshold designates the detection range. In addition, the weight of factor (n) is based on the number of biological pro-tumorigenic pathways factor (n) is known to be involved in. Specifically, a factor that affects several biological pro-tumorigenic pathways will receive a higher weight than a factor that affects only few biological pro-tumorigenic pathways. The weight is defined according to biological interpretation in the literature.

According to survival, patients were segregated into 2 groups. The Kaplan-Meier survival curve shown in FIG. 1 demonstrates that patients with an HR score above a value of 8 exhibited worse survival than those with a score equal to or below 8 (median survival 5 years vs 8 years; p value=0.001).

This example demonstrates that besides indicating a favorable or non-favorable response of a cancer patient to a certain cancer therapy, the method of the invention has also a predictive value with respect to patient outcome related to survival by quantifying the host response using a scoring method.

Example 8. Blocking of Chemotherapy-Induced IL-6 Improves the Efficacy of the Treatment

Materials:

InVivoMAb anti-mouse-IL-6, BioXCell, catalog #BE0046; Doxorubicin (DOX), Pharmacia Canada.

Cancer Cell Cultures:

Murine EMT6 breast carcinoma cells were purchased from the American Type Culture Collection (ATCC, USA). The cells were passaged in culture for no more than 4 months after being thawed from authentic stocks and were regularly tested and found to be mycoplasma-free (EZ-PCR mycoplasma test kit, Biological industries). Cells were cultured in Dulbecco's modified eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 1% L-glutamine, 1% sodium-pyruvate and 1% penicillin-streptomycin (Biological Industries, Israel), at 37° C. in 5% CO₂.

Statistical analysis: Data are expressed as mean±standard deviation (SD). The statistical significance of differences was assessed by two tailed unpaired T-test. For the tumor growth assessment, the statistical significance of differences was assessed by multiple T-test.

Example 8

To investigate the effect of chemotherapy on the level of IL-6 in the circulation, 7 weeks old naïve female BALB/c mice (n=3) were IP injected with 240 μg DOX. Control mice (n=3) were left untreated. One day after the injection, mice were bled by cardiac puncture and blood was collected into EDTA-coated tubes. Plasma was isolated by centrifugation of the whole blood at 1300 g for 10 minutes at room temperature. Supernatants (representing the plasma samples) were collected and the level of IL-6 in the plasma was determined by ELISA (IL-6 Quantikine ELISA Kit, R&D systems) according to the manufacturer's instruction. The results presented in FIG. 2A show that the plasma level of IL-6 was increased by 22-fold in response to DOX therapy compared to control.

To determine whether blocking host-derived IL-6 (that was upregulated in response to DOX) improves the efficacy of the treatment, 7 weeks old female BALB/c mice (Harlan, Israel) were orthotopically injected with 5×10⁵ EMT6 murine breast carcinoma cells into the mammary fat pad. Tumor size was assessed regularly with Vernier calipers using the formula width²×length×0.5. When tumors reached a size of 100 mm³, mice (n=5) were intraperitoneally (IP) injected with 240 μg DOX, 200 μg InVivoMAb anti-mouse-IL-6a (every 3 days, a total of 3 injections), or a combination of DOX with InVivoMAb anti-mouse-IL-6. Control mice (n=4) were left untreated. Tumor growth was monitored regularly and when tumors reached a size of 1500 mm³, mice were sacrificed. FIG. 2B demonstrates enhanced anti-tumor effect of the combined DOX and anti-IL-6 treatment compared to the control, DOX monotherapy and anti-IL-6 monotherapy. These results show that blocking chemotherapy-induced IL-6 improves treatment outcome.

APPENDIX

TABLE 1 List of 160 factors participating in the antibody array screen performed with plasma from human subjects receiving chemotherapy Human Cytokine Human Cytokine Human Cytokine Human Cytokine Array GS4000; Array GS2000; Array GS2000; Array GS2000; GSH-CYT-1 GSH-CHE-1 chip GSH-GF-1 chip GSH-INF-3 chip chip (RayBiotech) (RayBiotech) (RayBiotech) (RayBiotech) 6Ckine AR BLC Activin A Axl BDNF Eotaxin AgRP BTC bFGF Eotaxin-2 ANG CCL28 BMP-4 G-CSF ANG-1 CTACK BMP-5 GM-CSF Angiostatin CXCL16 BMP-7 I-309 CathepsinS ENA-78 b-NGF ICAM-1 CD 40 Eotaxin-3 EGF ICAM-1 Cripto-1 GCP-2 EGF R IL-1a DAN GRO EG-VEGF IL-1b DKK-1 HCC-1 FGF-4 IL-1ra E-Cadherin HCC-4 FGF-7 IL-2 EpCAM IL-9 GDF-15 IL-4 FAS L IL-17F GDNF IL-5 Fcr RIIB/C IL-18 BPa GH IL-6 Follistatin IL-28A HB-EGF IL-6Sr Galectin-7 IL-29 HGF IL-7 ICAM-2 IL-31 IGFBP-1 IL-8 IL-13 R1 IP-10 IGFBP-2 IL-10 IL-13 R2 I-TAC IGFBP-3 IL-11 IL-17B LIF IGFBP-4 IL-12p40 IL-2 Ra LIGHT IGFBP-6 IL-12p70 IL-2 Rb Lymphotactin IGF-I IL-13 IL-23 MCP-2 Insulin IL-15 LAP MCP-3 MCSF R IL-16 NrCAM MCP-4 NGF R IL-17 PAI-I MDC NT-3 MCP-1 PDGF-AB MIF NT-4 MCSF Resistin MIP-3a OPG MIG SDF-1b MIP-3b PDGF-AA MIP-1a sgp130 MPIF-1 PIGF MIP-1b Shh N MSPa SCF MIP-1d Siglec-5 NAP-2 SCF R PDGF-BB ST2 OPN TGFa RANTES TGF-b2 PARC TGFb TIMP-1 Tie-2 PF4 TGFb3 TIMP-2 TPO SDF-1a VEGF TNFa TRAIL-R4 TARC VEGF R2 TNFb TREM-1 TECK VEGF R3 TNF RI VEGF R1 TSLP VEGF-D TNF RII VEGF-C

TABLE 2A Summary of fold changes in the levels of circulating factors in breast cancer patients treated with AC chemotherapy Breast cancer patients treated with AC chemotherapy (N = 16) Fold change > 1.5 Fold change < 0.5 Average fold Average Factor % patients change % patients fold change 6Ckine 6.3 5.0 18.8 0.4 Activin A 18.8 2.3 12.5 0.4 AgRP 6.3 3.3 0.0 N/A ANG 6.3 1.5 0.0 N/A ANG-1 37.5 2.7 25.0 0.4 Angiostatin 6.3 2.0 0.0 N/A AR 12.5 1.9 18.8 0.4 Axl 50.0 2.7 0.0 N/A BDNF 37.5 2.2 6.3 0.5 bFGF 0.0 N/A 0.0 N/A BLC 75.0 4.7 0.0 N/A BMP-4 6.3 2.1 0.0 N/A BMP-5 12.5 1.8 0.0 N/A BMP-7 12.5 2.1 0.0 N/A b-NGF 6.3 1.6 18.8 0.4 BTC 12.5 11.6  0.0 N/A CathepsinS 0.0 N/A 18.8 0.4 CCL28 6.3 17.8  0.0 N/A CD 40 0.0 N/A 12.5 0.3 Cripto-1 6.3 1.7 0.0 N/A CTACK 6.3 2.7 6.3 0.5 CXCL16 0.0 N/A 0.0 N/A DAN 12.5 2.0 0.0 N/A DKK-1 0.0 N/A 12.5 0.4 E-Cadherin 0.0 N/A 0.0 N/A EGF 18.8 3.2 31.3 0.4 EGF R 0.0 N/A 0.0 N/A EG-VEGF 0.0 N/A 0.0 N/A ENA-78 37.5 3.3 0.0 N/A Eotaxin 6.3 1.7 25.0 0.4 Eotaxin-2 0.0 N/A 12.5 0.3 Eotaxin-3 31.3 3.1 6.3 0.1 EpCAM 25.0 1.9 6.3 0.5 FAS L 6.3 3.0 0.0 N/A Fcr RIIB/C 31.3 2.6 0.0 N/A FGF-4 12.5 1.8 0.0 N/A FGF-7 18.8 1.8 6.3 0.4 Follistatin 25.0 2.3 0.0 N/A Galectin-7 25.0 2.0 6.3 0.5 GCP-2 37.5 2.6 12.5 0.3 G-CSF 50.0 2.4 6.3 0.5 GDF-15 100.0 6.2 0.0 N/A GDNF 0.0 N/A 12.5 0.4 GH 31.3 2.1 18.8 0.2 GM-CSF 6.3 1.5 0.0 N/A GRO 31.3 2.6 0.0 N/A HB-EGF 6.3 1.6 0.0 N/A HCC-1 12.5 1.6 0.0 N/A HCC-4 25.0 1.6 12.5 0.3 HGF 6.3 1.6 0.0 N/A I-309 25.0 2.4 31.3 0.4 ICAM-1 6.3 1.9 0.0 N/A ICAM-2 12.5 2.0 0.0 N/A IFNg 0.0 N/A 0.0 N/A IGFBP-1 18.8 1.9 37.5 0.3 IGFBP-2 12.5 1.8 0.0 N/A IGFBP-3 6.3 4.1 0.0 N/A IGFBP-4 0.0 N/A 0.0 N/A IGFBP-6 6.3 2.2 0.0 N/A IGF-I 0.0 N/A 0.0 N/A IL-10 0.0 N/A 0.0 N/A IL-11 31.3 2.0 0.0 N/A IL-12p40 18.8 3.2 31.3 0.3 IL-12p70 18.8 2.2 6.3 0.5 IL-13 6.3 2.7 0.0 N/A IL-13 R1 31.3 1.7 6.3 0.5 IL-13 R2 18.8 1.9 6.3 0.5 IL-15 6.3 2.9 0.0 N/A IL-16 25.0 2.0 6.3 0.4 IL-17 25.0 2.4 6.3 0.4 IL-17B 50.0 2.1 0.0 N/A IL-17F 31.3 1.7 6.3 0.3 IL-18 BPa 18.8 5.5 12.5 0.3 IL-1a 31.3 2.6 12.5 0.4 IL-1b 25.0 1.9 12.5 0.5 IL-1ra 25.0 2.6 0.0 N/A IL-2 18.8 1.6 0.0 N/A IL-2 Ra 12.5 2.7 6.3 0.4 IL-2 Rb 18.8 1.9 6.3 0.5 IL-23 31.3 2.8 12.5 0.5 IL-28A 37.5 3.6 6.3 0.4 IL-29 6.3 5.1 0.0 N/A IL-31 12.5 2.0 0.0 N/A IL-4 12.5 1.8 0.0 N/A IL-5 6.3 15.2  0.0 N/A IL-6 6.3 1.9 0.0 N/A IL-6sR 6.3 1.6 0.0 N/A IL-7 12.5 2.3 0.0 N/A IL-8 18.8 1.7 0.0 N/A IL-9 6.3 11.2  0.0 N/A Insulin 25.0 1.8 0.0 N/A IP-10 25.0 3.5 12.5 0.3 I-TAC 18.8 3.9 37.5 0.3 LAP 31.3 1.9 6.3 0.5 LIF 12.5 4.9 12.5 0.2 LIGHT 6.3 3.5 6.3 0.5 Lymphotactin 25.0 2.4 6.3 0.4 MCP-1 0.0 N/A 12.5 0.4 MCP-2 18.8 1.7 6.3 0.4 MCP-3 31.3 2.4 0.0 N/A MCP-4 6.3 9.7 12.5 0.4 MCSF 43.8 24.5 18.8 0.3 MCSF R 0.0 N/A 0.0 N/A MDC 12.5 3.9 37.5 0.3 MIF 43.8 3.3 18.8 0.3 MIG 31.3 3.0 18.8 0.5 MIP-1a 12.5 1.9 12.5 0.4 MIP-1b 0.0 N/A 0.0 N/A MIP-1d 37.5 2.1 12.5 0.4 MIP-3a 18.8 7.8 25.0 0.4 MIP-3b 0.0 N/A 62.5 0.4 MPIF-1 56.3 2.4 0.0 N/A MSPa 12.5 2.1 12.5 0.2 NAP-2 0.0 N/A 0.0 N/A NGF R 12.5 2.0 0.0 N/A NrCAM 18.8 2.1 0.0 N/A NT-3 18.8 1.5 6.3 0.4 NT-4 25.0 1.9 6.3 0.0 OPG 6.3 1.9 0.0 N/A OPN 12.5 3.7 12.5 0.3 PAI-I 6.3 1.8 6.3 0.5 PARC 18.8 1.6 0.0 N/A PDGF-AA 50.0 3.2 18.8 0.3 PDGF-AB 37.5 2.8 6.3 0.4 PDGF-BB 37.5 3.6 12.5 0.3 PF4 18.8 1.6 0.0 N/A PIGF 12.5 2.2 12.5 0.4 RANTES 31.3 2.6 6.3 0.3 Resistin 18.8 1.8 6.3 0.4 SCF 12.5 2.1 12.5 0.4 SCF R 0.0 N/A 0.0 N/A SDF-1a 0.0 N/A 6.3 0.5 SDF-1b 6.3 2.1 12.5 0.3 sgp130 6.3 1.5 6.3 0.2 Shh N 6.3 2.2 0.0 N/A Siglec-5 12.5 1.5 0.0 N/A ST2 68.8 2.5 0.0 N/A TARC 25.0 2.0 0.0 N/A TECK 12.5 3.1 0.0 N/A TGFa 18.8 2.1 12.5 0.5 TGFb 31.3 1.6 0.0 N/A TGF-b2 6.3 1.8 6.3 0.5 TGFb3 18.8 2.0 6.3 0.1 Tie-2 31.3 1.7 6.3 0.4 TIMP-1 12.5 2.2 0.0 N/A TIMP-2 6.3 1.9 0.0 N/A TNF RI 0.0 N/A 0.0 N/A TNF RII 0.0 N/A 0.0 N/A TNFa 12.5 1.7 0.0 N/A TNFb 12.5 1.7 0.0 N/A TPO 12.5 2.1 0.0 N/A TRAIL-R4 12.5 2.3 0.0 N/A TREM-1 18.8 1.7 0.0 N/A TSLP 43.8 1.8 6.3 0.4 VEGF A 18.8 1.9 12.5 0.1 VEGF R1 25.0 2.1 0.0 N/A VEGF R2 31.3 1.9 6.3 0.5 VEGF R3 0.0 N/A 12.5 0.4 VEGF-C 6.3 1.5 0.0 N/A VEGF-D 25.0 2.4 12.5 0.3

TABLE 2B Summary of fold changes in the levels of circulating factors in colorectal cancer patients treated with FOLFOX chemotherapy Colorectal cancer patients treated with FOLFOX chemotherapy (N = 19) Fold change > 1.5 Fold change < 0.5 Average fold Average fold Factor % patients change % patients change 6Ckine 5.3 1.6 42.1 0.4 Activin A 21.1 1.8 10.5 0.5 AgRP 10.5 1.6 5.3 0.4 ANG 26.3 2.2 5.3 0.4 ANG-1 0.0 N/A 10.5 0.4 Angiostatin 5.3 2.9 0.0 N/A AR 5.3 2.1 10.5 0.3 Axl 21.1 1.7 0.0 N/A BDNF 21.1 1.7 36.8 0.4 bFGF 5.3 1.7 21.1 0.4 BLC 57.9 3.5 10.5 0.5 BMP-4 10.5 2.2 31.6 0.3 BMP-5 10.5 1.7 10.5 0.3 BMP-7 5.3 1.6 5.3 0.4 b-NGF 5.3 2.0 26.3 0.3 BTC 0.0 N/A 10.5 0.4 CathepsinS 5.3 3.0 0.0 N/A CCL28 0.0 N/A 10.5 0.2 CD 40 0.0 N/A 15.8 0.4 Cripto-1 5.3 1.8 0.0 N/A CTACK 0.0 N/A 5.3 0.3 CXCL16 0.0 N/A 5.3 0.2 DAN 15.8 2.1 5.3 0.2 DKK-1 0.0 N/A 0.0 N/A E-Cadherin 5.3 1.6 5.3 0.5 EGF 10.5 1.8 15.8 0.3 EGF R 5.3 1.6 5.3 0.5 EG-VEGF 5.3 1.6 5.3 0.4 ENA-78 0.0 N/A 31.6 0.4 Eotaxin 10.5 1.5 10.5 0.3 Eotaxin-2 0.0 N/A 21.1 0.4 Eotaxin-3 0.0 N/A 10.5 0.4 EpCAM 10.5 1.5 5.3 0.3 FAS L 5.3 1.6 5.3 0.5 Fcr RIIB/C 31.6 2.4 0.0 N/A FGF-4 5.3 1.5 5.3 0.2 FGF-7 15.8 2.1 10.5 0.2 Follistatin 10.5 2.1 5.3 0.4 Galectin-7 0.0 N/A 21.1 0.4 GCP-2 0.0 N/A 10.5 0.4 G-CSF 36.8 3.6 21.1 0.4 GDF-15 78.9 2.9 0.0 N/A GDNF 10.5 2.8 5.3 0.2 GH 21.1 3.3 21.1 0.3 GM-CSF 0.0 N/A 15.8 0.3 GRO 31.6 2.0 0.0 N/A HB-EGF 5.3 1.5 31.6 0.2 HCC-1 5.3 1.6 10.5 0.4 HCC-4 5.3 4.3 5.3 0.5 HGF 15.8 3.8 15.8 0.5 I-309 10.5 1.7 36.8 0.3 ICAM-1 0.0 N/A 10.5 0.4 ICAM-2 5.3 3.4 10.5 0.4 IFNg 5.3 1.6 15.8 0.4 IGFBP-1 5.3 3.5 36.8 0.4 IGFBP-2 0.0 N/A 15.8 0.4 IGFBP-3 10.5 3.1 15.8 0.4 IGFBP-4 15.8 2.3 15.8 0.4 IGFBP-6 0.0 N/A 26.3 0.4 IGF-I 10.5 6.2 0.0 N/A IL-10 5.3 1.7 15.8 0.4 IL-11 5.3 2.1 21.1 0.4 IL-12p40 0.0 N/A 31.6 0.3 IL-12p70 5.3 2.9 10.5 0.3 IL-13 0.0 N/A 5.3 0.3 IL-13 R1 10.5 2.5 5.3 0.5 IL-13 R2 0.0 N/A 10.5 0.5 IL-15 0.0 N/A 10.5 0.4 IL-16 0.0 N/A 10.5 0.3 IL-17 15.8 6.1 10.5 0.4 IL-17B 5.3 1.6 10.5 0.4 IL-17F 0.0 N/A 5.3 0.5 IL-18 BPa 5.3 3.1 5.3 0.5 IL-1a 15.8 2.1 15.8 0.4 IL-1b 5.3 1.5 26.3 0.2 IL-1ra 5.3 2.1 15.8 0.4 IL-2 0.0 N/A 5.3 0.4 IL-2 Ra 10.5 2.6 15.8 0.4 IL-2 Rb 5.3 2.6 21.1 0.4 IL-23 10.5 5.1 5.3 0.4 IL-28A 10.5 2.0 10.5 0.4 IL-29 0.0 N/A 5.3 0.3 IL-31 5.3 1.6 5.3 0.5 IL-4 5.3 4.9 15.8 0.3 IL-5 0.0 N/A 15.8 0.3 IL-6 0.0 N/A 5.3 0.5 IL-6sR 5.3 1.8 5.3 0.4 IL-7 0.0 N/A 15.8 0.4 IL-8 0.0 N/A 15.8 0.4 IL-9 5.3 10.5  0.0 N/A Insulin 15.8 14.4  21.1 0.3 IP-10 10.5 2.3 42.1 0.4 I-TAC 5.3 2.8 31.6 0.4 LAP 10.5 2.7 10.5 0.4 LIF 15.8 2.3 21.1 0.4 LIGHT 5.3 2.0 5.3 0.3 Lymphotactin 5.3 1.7 5.3 0.4 MCP-1 15.8 1.9 21.1 0.4 MCP-2 5.3 1.7 5.3 0.4 MCP-3 5.3 1.6 21.1 0.4 MCP-4 0.0 N/A 10.5 0.3 MCSF 21.1 4.0 26.3 0.4 MCSF R 10.5 1.7 10.5 0.4 MDC 0.0 N/A 10.5 0.3 MIF 5.3 7.5 21.1 0.3 MIG 15.8 1.7 26.3 0.3 MIP-1a 0.0 N/A 26.3 0.3 MIP-1b 5.3 1.6 15.8 0.3 MIP-1d 31.6 2.0 0.0 N/A MIP-3a 21.1 2.1 10.5 0.4 MIP-3b 26.3 2.7 52.6 0.3 MPIF-1 0.0 N/A 26.3 0.3 MSPa 15.8 1.9 15.8 0.3 NAP-2 5.3 4.3 10.5 0.2 NGF R 21.1 2.3 10.5 0.3 NrCAM 10.5 3.6 15.8 0.5 NT-3 5.3 9.9 15.8 0.4 NT-4 10.5 1.9 31.6 0.3 OPG 10.5 4.1 10.5 0.3 OPN 15.8 2.7 15.8 0.3 PAI-I 21.1 2.4 5.3 0.4 PARC 5.3 2.0 15.8 0.3 PDGF-AA 10.5 2.0 47.4 0.3 PDGF-AB 5.3 2.8 10.5 0.4 PDGF-BB 10.5 2.3 26.3 0.3 PF4 0.0 N/A 15.8 0.4 PIGF 10.5 2.2 21.1 0.3 RANTES 10.5 1.9 15.8 0.4 Resistin 36.8 2.0 0.0 N/A SCF 26.3 1.6 10.5 0.3 SCF R 5.3 4.1 15.8 0.4 SDF-1a 5.3 1.6 57.9 0.3 SDF-1b 15.8 3.1 0.0 N/A sgp130 15.8 2.3 0.0 N/A Shh N 10.5 3.2 10.5 0.4 Siglec-5 15.8 2.7 5.3 0.5 ST2 36.8 9.6 5.3 0.4 TARC 26.3 4.7 10.5 0.3 TECK 0.0 N/A 21.1 0.3 TGFa 5.3 1.8 31.6 0.3 TGFb 5.3 2.3 15.8 0.3 TGF-b2 10.5 3.4 5.3 0.4 TGFb3 15.8 62.0  31.6 0.1 Tie-2 10.5 3.3 5.3 0.5 TIMP-1 0.0 N/A 5.3 0.3 TIMP-2 0.0 N/A 5.3 0.5 TNF RI 21.1 1.7 10.5 0.3 TNF RII 5.3 1.5 10.5 0.2 TNFa 10.5 139.0  21.1 0.4 TNFb 5.3 1.6 15.8 0.4 TPO 15.8 3.0 10.5 0.5 TRAIL-R4 21.1 2.4 10.5 0.4 TREM-1 15.8 4.3 0.0 N/A TSLP 5.3 2.0 31.6 0.3 VEGF A 10.5 3.8 42.1 0.2 VEGF R1 5.3 4.3 21.1 0.4 VEGF R2 5.3 3.5 15.8 0.4 VEGF R3 15.8 2.3 21.1 0.3 VEGF-C 10.5 2.2 5.3 0.4 VEGF-D 10.5 2.6 21.1 0.2

TABLE 3 Profile of circulating factors indicating a host response to chemotherapy in human subjects 6Ckine (CCL21) Activin A ANG (Angiogenin) ANG-1 (Angiopoeitin-1) Amphiregulin (AR) Axl BDNF bFGF BLC (CXCL13) BMP-4 b-NGF CathepsinS EGF ENA-78 (CXCL5) Eotaxin (CCL11) Eotaxin-2 (CCL24) Eotaxin-3 (CCL26) EpCAM Fcr RIIB/C FGF-7 Follistatin Galectin-7 GCP-2 G-CSF GDF-15 GH GRO HB-EGF HCC-4 (CCL16) I-309 (CCL1) IGFBP-1 IGFBP-6 IL-11 IL-12p40 IL-12p70 IL-13 R1 IL-13 R2 IL-16 IL-17 IL-17B IL-17F IL-18 BPa IL-1α IL-1β IL-1ra IL-2 IL-2 Rb IL-23 IL-28A IL-8 IP-10 (CXCL10) I-TAC (CXCL11) LAP LIF Lymphotactin MCP-1 (CCL2) MCP-2 (CCL8) MCP-3 (CCL7) MCSF MDC (CCL22) MIF MIG (CXCL9) MIP-1α (CCL3) MIP-1δ (CCL15) MIP-3α (CCL20) MIP-3β (CCL19) MPIF-1 NGF R NrCAM NT-3 NT-4 PAI-I PARC PDGF-AA PDGF-AB PDGF-BB PF4 (CXCL4) PIGF RANTES (CCL5) Resistin SCF SDF-1α (CXCL12) ST2 (IL-1R4) TARC (CCL17) TECK TGFα TGFβ TGFβ3 Tie-2 TNF RI TNFα TRAIL-R4 TREM-1 TSLP VEGF VEGF R1 VEGF R2 VEGF R3 VEGF-D

TABLE 4 List of 308 factors participating in the antibody array screen performed with plasma from mice receiving chemotherapy or bortezomib Mouse L308 Array (RayBiotech; Cat no: AAM-BLG-1-2) 6Ckine, Activin A, Activin C, Activin RIB/ALK-4, Adiponectin/Acrp30, AgRP, ALCAM, Angiopoietin-like 2, Angiopoietin-like 3, AREG (Amphiregulin), Artemin, Axl, bFGF, B7-1/CD80, BAFF R/TNFRSF13C, BCMA/TNFRSF17, beta-Catenin, BLC, BTC (Betacellulin), Cardiotrophin-1, CCL1/I-309/TCA-3, CCL28, CCL4/ MIP-1 beta, CCL7/MCP-3/MARC, CCL8/MCP-2, CCR10, CCR3, CCR4, CCR6, CCR7, CCR9, CD11b, CD14, CRP, CD27/TNFRSF7, CD27 Ligand/TNFSF7, CD30, CD30 L, CD40, CD40 Ligand/TNFSF5, Cerberus 1, Chordin-Like 2, Coagulation Factor III/Tissue Factor, Common gamma Chain/IL-2 R gamma, CRG-2, Cripto, Crossveinless-2, Cryptic, Csk, CTACK, CTLA-4/CD152, CXCL14/ BRAK, CXCL16, CXCR2/IL-8 RB, CXCR3, CXCR4, CXCR6, DAN, Decorin, DKK-1, Dkk-3, Dkk-4, DPPIV/CD26, DR3/TNFRSF25, Dtk, EDAR, EGF R, EG- VEGF/PK1, Endocan, Endoglin/CD105, Endostatin, Eotaxin, Eotaxin-2, Epigen, Epiregulin, Erythropoietin (EPO), E-Selectin, FADD, FAM3B, Fas/TNFRSF6, Fas Ligand, FCrRIIB/CD32b, FGF R3, FGF R4, FGF R5 beta, FGF-21, Fit-3 Ligand, FLRG (Follistatin), Follistatin-like 1, Fractalkine, Frizzled-1, Frizzled-6, Frizzled-7, Galectin-3, G-CSF, GDF-1, GDF-3, GDF-5, GDF-8, GDF-9, GFR alpha-2/GDNF R alpha-2, GFR alpha-3/GDNF R alpha-3, GFR alpha-4/GDNF R alpha-4, GITR, GITR Ligand/TNFSF18, Glut2, GM-CSF, Granzyme B, Granzyme D, Granzyme G, Gremlin, Growth Hormone R, HGF R, HGF, HVEM/TNFRSF14, ICAM-1, ICAM-2/ CD102, ICAM-5, ICK, IFN-alpha/beta R1, IFN-alpha/beta R2, IFN-beta, IFN- gamma, IFN-gamma R1, IGFBP-1, IGFBP-2, IGFBP-3, IGFBP-5, IGFBP-6, IGFBP- rp1/IGFBP-7, IGF-I, IGF-II, IL-1 alpha, IL-1 beta, IL-1 R4/ST2, IL-1 R6/IL-1 R rp2, IL-1 R9, IL-1 RI, IL-1 RII, IL-2, IL-2 R alpha, IL-2 R beta, IL-3, IL-3 R alpha, IL-3 R beta, IL-4, IL-4 R, IL-5, IL-5 R alpha, IL-6, IL-6 R, IL-7, IL-7 R alpha, IL-9, IL-9 R, IL-10, IL-10 R alpha, IL-11, IL-12 p40/p70, IL-12 p70, IL-12 R beta 1, IL-13, IL-13 R alpha 2, IL-15, IL-15 R alpha, IL-16, IL-17, IL-17B R, IL-17C, IL-17D, IL- 17E, IL-17F, IL-17R, IL-17RC, IL-17RD, IL-18 R alpha/IL-1 R5, IL-20, IL-20 R alpha, IL-21, IL-21 R, IL-22, IL-22BP, IL-23, IL-23 R, IL-24, IL-27, IL-28/IFN- lambda, IL-31, IL-31 RA, Insulin, Integrin beta 2/CD18, I-TAC, KC, Kremen-1, Kremen-2, Lefty-1, Leptin R, LEPTIN(OB), LIF, LIGHT/TNFSF14, LIX, LRP-6, L-Selectin, Lungkine, Lymphotactin, Lymphotoxin beta R/TNFRSF3, MAdCAM-1, MCP-1, MCP-5, M-CSF, MDC, MFG-E8, MFRP, MIG, MIP-1 alpha, MIP-1 gamma, MIP-2, MIP-3 alpha, MIP-3 beta, MMP-2, MMP-3, MMP-9, MMP-12, MMP-14/ LEM-2, MMP-24/MT5-MMP, Neuregulin-3/NRG3, Neurturin, NGF R/ TNFRSF16, NOV/CCN3, Osteoactivin/GPNMB, Osteopontin, Osteoprotegerin, OX40 Ligand/TNFSF4, PDGF C, PDGF R alpha, PDGF R beta, Pentraxin3/TSG- 14, PF-4, PlGF-2, Progranulin, Prolactin, P-Selectin, RAGE, RANTES, RELM beta, Resistin, S100A10, SCF, SCF R/c-kit, SDF-1, Serum Amyloid A1, Shh-N, SIGIRR, SLPI, Soggy-1, SPARC, Spinesin Ectodomain, TACI/TNFRSF13B, TARC, TCA-3, TCCR/WSX-1, TECK, TFPI, TGF-beta 1, TGF-beta 2, TGF-beta 3, TGF-beta RI/ ALK-5, TGF-beta RII, Thrombospondin, Thymus Chemokine-1, Tie-2, TIMP-1, TIMP-2, TIMP-4, TL1A/TNFSF15, TLR1, TLR2, TLR3, TLR4, TMEFF1/ Tomoregulin-1, TNF RI/TNFRSF1A, TNF RII, TNF-alpha, TNF-beta/TNFSF1B, TPO, TRAIL/TNFSF10, TRAIL R2/TNFRSF10B, TRANCE/TNFSF11, TREM- 1, TROY, TSLP, TSLP R, TWEAK/TNFSF12, TWEAK R/TNFRSF12, Ubiquitin, uPAR, Urokinase, VCAM-1, VE-Cadherin, VEGF, VEGF R1, VEGF R2, VEGF R3, VEGF-B, VEGF-C, VEGF-D, WIF-1, WISP-1/CCN4

TABLE 5 Summary of fold changes in the levels of circulating factors in chemotherapy-treated vs control BALB/c mice Fold change (chemotherapy- treated vs control) Paclitaxel FOLFOX Cardiotrophin-1 >10 NC CRP >10 NC CRG-2 >10 NC Cripto >10 0.2 CTACK NC 0.5 CXCL14/BRAK 2.6 3.5 CXCL16 4.4 0.3 CXCR2/IL-8 RB 2.0 NC CXCR6 3.6 NC Dkk-3 >10 NC Endocan 4.4 NC Endostatin 4.9 NC Eotaxin-2 3.4 >10 Erythropoietin (EPO) 3.9 NC FCrRIIB/CD32b NC 0.2 Frizzled-6 2.2 NC Frizzled-7 6.1 NC GDF-5 NC >10 GFR alpha-4/GDNF R alpha-4 NC 0.2 GITR >10 NC GM-CSF 2.4 NC HVEM/TNFRSF14 NC >10 IGFBP-1 >10 NC IL-1 alpha >10 NC IL-1 R4/ST2 1.9 >10 IL-3 R alpha >10 NC IL-7 R alpha 8.8 NC IL-9 R 5.7 NC IL-10 NC >10 IL-11 NC >10 IL-12 p70 >10 1.5 IL-15 2.5 >10 IL-15 R alpha 3.4 >10 IL-17 NC >10 IL-17R 2.6 NC IL-18 R alpha/IL-1 R5 >10 NC IL-20 >10 NC IL-23 R 1.8 NC IL-27 2.0 NC IL-28/IFN-lambda 5.4 >10 IL-31 >10 NC LIF >10 NC LIX >10 NC LRP-6 >10 NC Lungkine 2.0 NC Lymphotoxin beta R/TNFRSF3 1.6 NC MAdCAM-1 >10 NC MCP-1 >10 NC M-CSF 1.8 NC MIP-1 gamma >10 NC MIP-2 3.1 >10 MMP-9 4.3 NC PF-4 3.6 NC Prolactin 4.1 NC P-Selectin >10 NC SDF-1 >10 NC SLPI NC >10 Soggy-1 3.6 >10 TACI/TNFRSF13B >10 NC TARC >10 NC TCA-3 3.0 >10 TGF-beta 1 NC >10 TGF-beta 2 3.4 NC TGF-beta RII 2.2 NC Thrombospondin >10 >10 Thymus Chemokine-1 >10 NC TNF-alpha 2.8 NC TNF-beta/TNFSF1B 2.9 >10 TRAIL/TNFSF10 NC >10 TPO >10 NC TWEAK R/TNFRSF12 >10 NC VEGFC >10 NC WISP-1/CCN4 3.7 >10 NC, no change

TABLE 6 Summary of fold changes in the levels of circulating factors in bortezomib-treated vs control BALB/c mice Fold change (bortezomib-treated vs control) CCL28 3.9 CCR9 1.9 CD11b 2.6 CRP 3.1 CD27/TNFRSF7 1.5 CTACK 2.0 Dtk 6.6 EG-VEGF/PK1 1.5 Fas/TNFRSF6 2.2 FCrRIIB/CD32b 5.5 FGF R5 beta 3.0 Follistatin-like 1 >10 Frizzled-6 5.9 GDF-8 2.3 GFR alpha-4/GDNF R alpha-4 6.4 Glut2 1.9 HVEM/TNFRSF14 2.3 ICAM-1 3.3 IFN-beta 6.9 IFN-gamma 2.1 IFN-gamma R1 1.4 IGFBP-1 2.2 IGFBP-3 3.6 IL-1 alpha 2.5 IL-1 R4/ST2 1.8 IL-1 RI 3.5 IL-3 2.5 IL-5 4.1 IL-6 4.6 IL-6 R >10 IL-10 >10 IL-11 3.1 IL-12 p70 1.6 IL-12 R beta 1 >10 IL-13 >10 IL-17BR >10 IL-17C >10 IL-17E >10 IL-31 >10 IL-31 RA >10 Lungkine 4.9 Lymphotoxin beta R/TNFRSF3 1.9 MCP-1 2.6 M-CSF >10 MIP-3 beta >10 Neuregulin-3/NRG3 >10 Osteoporotegerin >10 PlGF-2 10.0 RAGE >10 TECK >10 TGF-beta 3 >10 Thymus Chemokine-1 >10 TL1A/TNFSF15 >10 TLR4 >10 TPO >10 TRANCE/TNFSF11 3.7 TROY >10 VEGF-D >10

TABLE 7 List of 53 factors participating in the antibody array screen performed with plasma from irradiated or post-surgery mice Proteome Profiler Mouse Angiogenesis Array kit (R&D Systems; Cat no: ARY015) ADAMTS1/METH1 IP-10/CXCL10 AR KC/CXCL1 ANG Leptin Ang-1 MCP-1 Ang-3 MIP-1α/CCL3 Coagulation Factor III/TF MMP-3 CXCL16 MMP-8 Cyr61/CCN1 MMP-9 DLL4 NOV/CCN3 DPPIV/CD26 OPN EGF PD-ECGF Endoglin/CD105 PDGF-AA Endostatin PDGF-BB ET-1 Pentraxin-3/TSG-14 FGF-1 Platelet Factor 4/CXCL4 FGF-2 PLGF-2 FGF-7 PRL Fractalkine/CX3CL1 Proliferin GM-CSF SDF-1 HB-EGF PAI-1 HGF PEDF IGFBP-1 TSP-2 IGFBP-2 TIMP-1 IGFBP-3 TIMP-4 IL-1alfa VEGF IL1 beta VEGF-B IL-10

TABLE 8 Summary of fold changes in the levels of circulating factors in 2Gy-irradiated vs control BALB/c mice Fold change (Irradiated vs control) ANG 2.5 Ang-1 4.3 Cyr61/CCN1 4.1 DPPIV/CD26 2.1 EGF 2.6 Endoglin/CD105 4.0 FGF-1 3.8 IL-10 2.1 Leptin 3.2 MCP-1 2.9 MMP-3 3.0 PDGF-AA 2.9 PDGF-BB 4.2 Pentraxin-3/TSG-14 3.0 PLGF-2 3.0 SDF-1 5.5 TIMP-1 4.3

TABLE 9 Summary of fold changes in the levels of circulating factors in post-surgery vs control BALB/c mice Fold change (surgery vs control) Ang-1 5.9 TF 0.1 FGF-1 6.6 CX3CL1 2.3 MCP-1 0.5 PD-ECGF 0.2 PDGF-AA 3.5 PDGF-BB 4.0 PLGF-2 1.9 PRL 7.5 TSP-2 0.2 TIMP-1 0.2

TABLE 10 List of 111 factors participating in the antibody array screen performed with plasma from mice receiving anti-PD-1 therapy Proteome Profiler Mouse XL Cytokine Array (R&D Systems; Cat no: ARY028) Adiponectin/Acrp30 Amphiregulin Angiopoietin-1 Angiopoietin-2 Angiopoietin-like 3 BAFF/BLyS/TNFSF13B C1q R1/CD93 CCL2/JE/MCP-1 CCL3/CCL4 MIP-1 alpha/beta CCL5/RANTES CCL6/C10 CCL11/Eotaxin CCL12/MCP-5 CCL17/TARC CCL19/MIP-3 beta CCL20/MIP-3 alpha CCL21/6Ckine CCL22/MDC CD14 CD40/TNFRSF5 CD160 Chemerin Chitinase 3-like 1 Coagulation Factor III/Tissue Factor Complement Component C5/C5a Complement Factor D C-Reactive Protein/CRP CX3CL1/Fractalkine CXCL1/KC CXCL2/MIP-2 CXCL9/MIG CXCL10/IP-10 CXCL11/I-TAC CXCL13/BLC/BCA-1 CXCL16 Cystatin C Dkk-1 DPPIV/CD26 EGF Endoglin/CD105 Endostatin Fetuin A/AHSG FGF acidic FGF-21 Flt-3 Ligand Gas6 G-CSF GDF-15 GM-CSF HGF ICAM-1/CD54 IFN-gamma IGFBP-1 IGFBP-2 IGFBP-3 IGFBP-5 IGFBP-6 IL-1 alpha/IL1F1 IL-1 beta/IL-1F2 IL-1ra/IL-1F3 IL-2 IL-3 IL-4 IL-5 IL-6 IL-7 IL-10 IL-11 IL-12p40 IL-13 IL-15 IL-17A IL-22 IL-23 IL-27 IL-28 IL-33 LDL R Leptin LIF Lipocalin-2/NGAL LIX M-CSF MMP-2 MMP-3 MMP-9 Myeloperoxidase Osteopontin (OPN) Osteoprotegerin/TNF RSF11B PD- ECGF/Thymidine phosphorylase PDGF-BB Pentraxin 2/SAP Pentraxin 3/TSG-14 Periostin/OSF-2 Pref-1/DLK-1/FA1 Proliferin Proprotein Convertase 9/PCSK9 RAGE RBP4 Reg3G Resistin E-Selectin/CD62E P-Selectin/CD62P Serpin E1/PAI-1 Serpin F1/PEDF Thrombopoietin TIM-1/KIM- 1/HAVCR TNF-alpha VCAM-1/CD106 VEGF WISP-1/CCN4

TABLE 11 Summary of fold changes in the levels of circulating factors in anti-PD1-treated vs control BALB/c mice Fold change (anti-PD-1 vs IgG) C14 8.0 CCL17/TARC 5.0 CCL19/MIP-3β 1.5 CCL21/6Ckine 1.7 CCL3/CCL4/MIP-1α/β 1.8 CCL5/RANTES 13.0 CD40/TNFRSF5 3.3 Chemerin 3.6 Chitinase 3-like 1 2.6 CXCL13/BCL/BCA-1 1.8 CXCL9/MIG 1.7 Cystatin C 21.2 DKK-1 5.2 Endoglin/CD105 2.8 E-Selectin/CD62E 1.6 Fetuin A/AHSG 14.6 FGF acidic 1.7 FGF-21 2.5 Gas 6 2.1 G-CSF 2.9 GM-CSF 2.2 HGF 3.9 IFN-γ 1.9 IL-10 7.2 IL-12 p40 23.5 IL-13 2.5 IL-1rα/IL-1F3 3.1 IL-2 5.5 IL-22 2.4 IL-27 p28 2.3 IL-28A/B 2.0 IL-33 3.0 IL-4 1.5 IL-6 15.6 IL-7 5.2 LDL R 8.1 Leptin 2.0 LIF 1.8 Lipocalin-2/NGAL 4.8 M-CSF 6.9 MMP-9 5.4 Myeloperoxidase 6.7 Osteprotegerin/TNFRS11B 1.8 PDGF-BB 4.1 Pentraxin 2/SAP 2.7 Pentraxin 3/TSG-14 3.3 Periostin/TSG-14 2.0 Pref-1/DLK-1/FA1 5.8 Proliferin 5.8 RBP4 4.5 Serpin E1/PAI-1 3.8 Serpin F1/PAI-1 1.6 TIM-1/KIM-1/HAVCR 1.7 TNF-α 4.3 VCAM-1/CD106 1.6 VEGF 0.3 WISP-1/CCN4 3.0

TABLE 12 List of 200 factors participating in the antibody array screen performed with plasma from mice receiving immune-checkpoint inhibitor (anti-PD-1 or anti-PD-L1) therapy Quantibody Mouse Cytokine Array (RayBiotech; Cat no: QAM-CAA-4000) 4-1BB (TNFRSF9/CD137); 6Ckine (CCL21); ACE; Activin A; ADAMTS1 (METH1); Adiponectin; ALK-1; Amphiregulin; ANG-3; ANGPTL3; Artemin; Axl; B7-1; BAFF R; bFGF; BLC (CXCL13); BTC; C5a; CCL28; CCL6; CD27; CD27L; CD30; CD30L; CD36; CD40; CD40L; CD48; CD6; Chemerin; Chordin; Clusterin; CRP; Cardiotrophin-1; CTLA4; CXCL16; Cystatin C; DAN; Decorin; Dkk-1; DLL4; Dtk; E-Cadherin; EDAR; EGF; Endocan; Endoglin; Eotaxin (CCL11); Eotaxin-2 (CCL24); Epigen; Epiregulin; E-selectin; Fas; Fas L; Fcg RIIB; Fetuin A; Flt-3L; Fractalkine; Galectin-1; Galectin-3; Galectin-7; Gas 1; Gas 6; G-CSF; GITR; GITR L; GM-CSF; gp130; Granzyme B; Gremlin; H60; HAI-1; HGF; HGF R; ICAM-1; INFg; IFNg R1; IGF-1; IGFBP-2; IGFBP-3; IGFBP-5; IGFBP-6; IL-1 R4; IL-10; IL- 12p40; IL-12p70; IL-13; IL-15; IL-17; IL-17B; IL-17B R; IL-17E; IL-17F; IL-1a; IL- 1b; IL-1ra; IL-2; IL-2 Ra; IL-20; IL-21; IL-22; IL-23; IL-28; IL-3; IL-3 Rb; IL-33; IL-4; IL-5; IL-6; IL-7; IL-7 Ra; IL-9; I-TAC (CXCL11); JAM-A; KC (CXCL1); Kremen-1; Leptin; Leptin R; Limitin; Lipocalin-2; LIX; LOX-1; L-selectin; Lungkine; Lymphotactin; MadCAM-1; Marapsin; MBL-2; MCP-1 (CCL2); MCP-5; MCSF; MDC (CCL22); Meteorin; MFG-E8; MIG (CXCL9); MIP-1a (CCL3); MIP- 1b (CCL4); MIP-1g; MIP-2; MIP-3a (CCL20); MIP-3b (CCL19); MMP-10; MMP-2; MMP-3; Neprilysin; Nope; NOV; OPG; OPN; Osteoactivin; OX40 Ligand; P- Cadherin; PDGF-AA; Pentraxin 3; Periostin; Persephin; PF4 (CXCL4); PlGF-2; Progranulin; Prolactin; Pro-MMP-9; Prostasin; P-selectin; RAGE; RANTES (CCL5); Renin 1; Resistin; SCF; SDF-1a; sFRP-3; Shh-N; SLAM; TACI; TARC (CCL17); TCA-3; TCK-1 (CXCL7); TECK (CCL25); Testican 3; TGFb1; TIM-1; TNF RI; TNF RII; TNFa; TPO; TRAIL; TRANCE; TREM-1; TREML1; TROY; Tryptase epsilon; TSLP; TWEAK; TWEAK R; VACM-1; VEGF; VEGF R1; VEGF R2; VEGF R3; VEGF-B; VEGF-D

TABLE 13 Summary of fold changes in the levels of circulating factors in anti-PD-L1-treated vs control BALB/c and C57bl/6 mice Fold change (anti-PD-L1 vs IgG) BALB/c C57bl/6 Female Male Female Male ADAMTS1 1.6 0.5 2.1 1.9 ALK-1 2.3 1.5 6.0 0.6 Amphiregulin 2.7 2.8 3.0 0.9 Axl 2.7 2.2 2.3 1.9 CD30 2.4 2.3 1.5 1.5 Dkk-1 1.5 0.8 1.4 0.4 EGF 6.3 4.1 0.7 4.0 Eotaxin-2 1.8 1.7 1.0 0.8 Epiregulin 2.7 0.6 0.4 0.2 Fcg RIIB 2.3 1.5 1.4 0.9 Fractalkine 2.7 2.0 1.0 1.0 G-CSF 2.2 2.7 2.0 1.2 GITR L 8.2 7.4 1.4 0.3 Granzyme B 2.0 1.1 2.7 0.7 HGF 2.3 0.6 3.7 3.6 HGF R 10.4 1.7 24.9 2.4 IL-1ra 3.6 1.8 2.9 1.3 IL-33 1.3 2.2 1.6 1.0 IL-6 1.8 1.7 1.0 0.5 IL-7 1.7 1.6 1.1 0.0 I-TAC 6.1 7.4 4.2 1.1 Lipocalin-2 2.0 4.8 2.6 2.1 MadCAM-1 0.8 7.1 2.6 2.4 MCP-5 2.2 4.5 1.3 1.2 MDC 2.2 1.8 0.9 0.6 Meteorin 0.6 0.7 1.9 3.0 MFG-E8 1.8 2.6 4.3 1.8 MIG 1.6 1.2 1.9 1.4 MIP-3b 1.5 2.8 1.7 0.9 OPG 0.8 0.9 1.7 2.2 Osteoactivin 0.8 1.2 2.5 2.4 P-Cadherin 0.8 0.9 1.7 2.1 Pentraxin 3 1.3 1.6 3.0 2.7 Pro-MMP-9 3.0 2.2 1.1 1.3 SCF 2.6 3.3 4.5 3.4 TACI 2.7 2.9 2.3 1.3 TARC 1.4 1.6 1.5 0.5 TNF RII 1.3 2.0 1.6 2.6 TREM-1 2.8 1.9 7.2 3.1 TROY 2.3 1.7 6.7 6.1 VEGF R1 1.9 1.3 1.8 0.3

TABLE 14 Summary of fold changes in the levels of circulating factors in anti-PD1-treated vs control BALB/c and SCID mice Fold change (anti-PD-1 vs IgG) BALB/c SCID ADAMTS1 2.4 0.3 ALK-1 3.4 3.4 Amphiregulin 3.7 0.0 CD40L 3.6 0.9 Dkk-1 2.0 0.8 Epigen 2.3 1.8 IL-17B 3.4 0.3 IL-17B R 2.1 0.9 IL-1ra 8.7 1.5 IL-21 2.6 1.0 IL-22 9.1 0.0 IL-6 2.1 1.8 I-TAC 9.3 1.1 MFG-E8 2.8 0.6 Osteoactivin 2.5 2.0 SCF 2.0 0.0 TARC 1.5 0.9 TREM-1 3.9 0.3 TROY 1.7 0.7 VEGF R1 2.6 0.8

TABLE 15 Patients' characteristics Colorectal patients Characteristics N = 17 Sex, n (%) Female 8 (47) Male 9 (53) Age, mean (range) 59.6 (41-79) Stage, n (%) I-III 13 (76)  IV 4 (24)

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1. A method for predicting the response of a cancer patient to treatment with a cancer therapy, the method comprising the steps of: (i) performing an assay on a biological sample selected from blood plasma, whole blood, blood serum or peripheral blood mononuclear cells obtained from the cancer patient at a time period after a session of treatment with said cancer therapy, to determine the levels of one or more of a plurality of factors induced in the circulation of said cancer patient in response to treatment with said cancer therapy, said one or more of the plurality of factors promoting responsiveness or non-responsiveness of the cancer patient to the treatment with said cancer therapy; (ii) obtaining reference levels for each of the one or more of the plurality of the induced factors of step (i) in a biological sample selected from blood plasma, whole blood, blood serum or peripheral blood mononuclear cells, obtained from the cancer patient before said session of treatment with the cancer therapy; (iii) establishing the fold change for each of the one or more of the plurality of the induced factors of step (i) by comparing the level of each induced factor of step (i) with the reference level of step (ii) for the same factor; and (iv) determining that the cancer patient has a favorable or a non-favorable response to the treatment with said cancer therapy based on the fold change established in step (iii) for one or more of the plurality of induced factors of step (i).
 2. The method of claim 1, wherein the biological sample of steps (i) and (ii) is blood plasma.
 3. The method of claim 1, wherein said session of treatment with the cancer therapy is the first session of treatment with said cancer therapy, the biological sample of step (i) is obtained from the cancer patient at about 20, 24, 30, 36, 40, 48, 50, 60, 72 hours or more, including up to one to three weeks or more, after said first session of treatment, and the reference biological sample of step (ii) is obtained from the cancer patient at a time point including at about 72 hours or less, including at about 60, 50, 48, 40, 36, 30, 24 or 20 hours or just before said first session of treatment with the cancer therapy.
 4. The method of claim 1, wherein said session of treatment with the cancer therapy is one of multiple sessions of treatment that is not the first session of treatment with the cancer therapy, and the biological sample is obtained from the cancer patient at any time point between two consecutive sessions of treatment, wherein said biological sample is simultaneously the biological sample of step (i) and the reference biological sample according to step (ii) for the next session assay according to step (i).
 5. The method of claim 4, wherein the time between two consecutive sessions of treatment is from one day to one to three weeks, depending on the cancer therapy, and the biological sample is obtained from the cancer patient at about 20, 24, 30, 36, 40, 48, 50, 60, 72 hours or more, including up to one to three weeks or more, after the session of treatment that is not the first session of treatment with the cancer therapy.
 6. The method of claim 1, wherein the fold-change established in step (iii) is defined by a fold change of ≥1.5 indicating upregulation or a fold change of ≤0.5 indicating down-regulation in the level of each of the one or more of the plurality of factors induced in the circulation of the cancer patient in response to the treatment with the cancer therapy, these values being considered significant and predictive of a non-favorable or favorable response of the cancer patient to the treatment with said cancer therapy.
 7. The method of claim 6, wherein the prediction of a favorable or a non-favorable response of the cancer patient to the treatment with the cancer therapy is based on significant fold changes of one or more, optionally two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, or 20-25 or more, of the induced factors.
 8. The method of claim 1, wherein the factors induced in the circulation of said cancer patient in response to treatment with said cancer therapy are molecular factors including cytokines, chemokines, growth factors, enzymes and soluble receptors.
 9. The method of claim 8, wherein the molecular factors are pro-tumorigenic or pro-metastatic factors, and the pro-tumorigenic factors may be pro-angiogenic, pro-inflammatory/chemotactic or proliferative growth factors.
 10. The method of claim 9, wherein if there is an increase (up-regulation) of at least about 1.5-fold in the level of one or more of the pro-tumorigenic or pro-metastatic factors, then the prediction is of a non-favorable response of the cancer patient to the treatment with the cancer therapy, and if there is a decrease (down-regulation) of at least about 0.5-fold in the level of one or more of the pro-tumorigenic or pro-metastatic factors, then the prediction is of a favorable response of the cancer patient to the treatment with the cancer therapy.
 11. The method of claim 1, wherein the cancer therapy is a modality including chemotherapy, radiation therapy, surgery, targeted cancer therapy, hormonal therapy, thermotherapy, and combinations thereof.
 12. The method of claim 11, wherein the cancer therapy modality is chemotherapy as the sole therapy or chemotherapy in combination with another cancer therapy including surgery, radiation therapy or targeted cancer therapy.
 13. The method of claim 12, wherein the chemotherapy is carried out with one chemotherapeutic drug including paclitaxel, 5-fluorouracil, doxorubicin, gemcitabine and cyclophosphamide.
 14. The method of claim 12, wherein chemotherapy is carried out with a combination of 2 or 3 chemotherapeutic drugs chosen from: (i) anthracyclines including doxorubicin, pegylated liposomal doxorubicin, and epirubicin; (ii) taxanes including paclitaxel, albumin-bound paclitaxel and docetaxel; (iii) 5-fluorouracil; (iv) cyclophosphamide; (v) platinum agents including cisplatin, oxaliplatin and carboplatin; (vi) vinorelbine; (vii) capecitabine; (viii) gemcitabine; (ix) ixabepilone; and (x) eribulin.
 15. The method of claim 14, wherein said combination of 2 drugs includes doxorubicin (adriamycin) and cyclophosphamide (AC) and said combination of 3 drugs includes folinic acid, fluorouracil and oxaliplatin (FOLFOX).
 16. The method of claim 11, wherein the cancer therapy modality is radiation therapy alone or radiation in combination with surgery or chemotherapy.
 17. The method of claim 11, wherein the cancer therapy modality is surgery alone as the curative treatment or as the primary therapy in combination with radiation therapy or chemotherapy prior to or after the surgery.
 18. The method of claim 11, wherein the cancer therapy modality is a targeted cancer therapy selected from small-molecule drugs and monoclonal antibodies.
 19. The method of claim 18, wherein the small molecules include: (i) proteasome inhibitors including bortezomib, carfilzomib and ixazomib; (ii) tyrosine-kinase inhibitors (TKI) including dasatinib, erlotinib, gefitinib, imatinib mesylate, lapatinib, nilotinib, pazopanib, and sunitinib; and (iii) serine-threonine kinase (STK) inhibitors including dabrafenib, everolimus, temsirolimus, trametinib, and vemurafenib.
 20. The method of claim 18, wherein the targeted therapy is immunotherapy with a non-conjugated monoclonal antibody (mAb) including: alemtuzumab, bevacizumab, cetuximab, daratumumab, olaratumab, panitumumab, rituximab and trastuzumab; or with a monoclonal antibody conjugated to a chemotherapeutic drug or labeled with small radioactive particles.
 21. The method of claim 18, wherein the targeted therapy is anti-angiogenic therapy in which the antiangiogenic drug is either a monoclonal antibody that targets VEGF, including bevacizumab and panitumumab, or a tyrosine-kinase inhibitor including sunitinib that targets the VEGF receptors.
 22. The method of claim 1, wherein the factors induced by the cancer patient in response to the cancer therapy are pro-tumorigenic factors or pro-metastatic factors including: (i) the pro-angiogenic factors angiogenin; angiopoietin-1; angiopoietin-2; bNGF; cathepsin S; Galectin-7; GCP-2; G-CSF; GM-CSF; PAI-1; PDGF-AA; PDGF-BB; PDGF-AB; PlGF; PlGF-2; SDF-1; Tie2; VEGF-A; VEGF-C; VEGF-D; VEGF-R1; VEGF-R2; VEGF-R3; (ii) the pro-inflammatory and/or chemotactic factors 6Ckine; angiopoietin-1; angiopoietin-2; BLC; BRAK; CD186; ENA-78; Eotaxin-1; Eotaxin-2; Eotaxin-3; EpCAM; GDF-15; GM-CSF; GRO; HCC-4; I-309; IFN-γ; IL-1α; IL-1β; IL-1R4 (ST2); IL-2; IL-2R; IL-3; IL-3Rα; IL-5; IL-6; IL-6R; IL-7; IL-8; IL-8 RB; IL-11; IL-12; IL-12p40; IL-12p70; IL-13; IL-13 R1; IL-13R2; IL-15; IL-15Rα; IL-16; IL-17; IL-17C; IL-17E; IL-17F; IL-17R; IL-18; IL-18BPa; IL-18 Rα; IL-20; IL-23; IL-27; IL-28; IL-31; IL-33; IP-10; I-TAC; LIF; LIX; LRP6; MadCAM-1; MCP-1; MCP-2; MCP-3; MCP-4; M-CSF; MIF; MIG; MIP-1 gamma; MIP-1α; MIP-1β; MIP-1δ; MIP-3α; MIP-3β; MPIF-1; PARC; PF4; RANTES; Resistin; SCF; SCYB16; TACI; TARC; TSLP; TNF-α; TNF-R1; TRAIL-R4; TREM-1; (ii) the proliferative factors Activin A; Amphiregulin; Axl; BDNF; BMP4; cathepsin S; EGF; FGF-1; FGF-2; FGF-7; FGF-21; Follistatin; Galectin-7; Gas6; GDF-15; HB-EGF; HGF; IGFBP-1; IGFBP-3; LAP; NGF R; NrCAM; NT-3; NT-4; PAI-1; TGF-α; TGF-β; TGF-β3; TRAIL-R4; and (iv) the pro-metastatic factors ADAMTS1; cathepsin S; FGF-2; Follistatin; Galectin-7; GCP-2; GDF-15; IGFBP-6; LIF; MMP-9; pro-MMP9; RANK; RANKL; RANTES; SDF-1; and CXCR4.
 23. The method of claim 22, wherein the cancer therapy modality is chemotherapy and the induced factors indicating a host response to chemotherapy include: 6Ckine; Activin A; Amphiregulin; Angiogenin; Angiopoietin-1; Axl; BDNF; BLC; BMP4; bNGF; Cathepsin S; EGF; ENA-78; Eotaxin; Eotaxin-2; Eotaxin-3; EpCAM; Fcr RIIB/C; FGF-2; FGF-7; Follistatin; Galectin-7; GCP-2; G-CSF; GDF-15; GH; HB-EGF; HCC-4; I-309; IGFBP-1; IGFBP-6; IL-1α; IL-1β; IL-1ra; IL-2; IL-2 Rb; IL-8; IL-11; IL-12p40; IL-12p70; IL-13 R1; IL-13 R2; IL-16; IL-17; IL-17B; IL-17F; IL-18BPa; IL-23; IL-28A; IP-10; I-TAC; LAP; LIF; Lymphotactin; MCP-1; MCP-2; MCP-3; M-CSF; MDC; MIF; MIG; MIP-1α; MIP-1δ; MIP-3α; MIP-3β; MPIF-1; NGF-R; NrCAM; NT-3; NT-4; PAI-1; PARC; PDGF-AA; PDGF-AB; PDGF-BB; PF4; PlGF; PlGF-2; RANTES; Resistin; SCF; SDF-1α; ST2; TARC; TECK; TGFα; TGFβ; TGFβ3; Tie-2; TNFα; TNF-R1; TRAIL-R4; TREM-1; TLSP; VEGF; VEGF-D; VEGF-R1; VEGF-R2; and VEGF-R3.
 24. The method of claim 23, wherein the induced factors indicating a host response to chemotherapy with the combination Adriamycin/Cyclophosphamide (AC) or Folinic acid/Fluorouracil/Oxaliplatin (FOLFOX) include: (i) the pro-angiogenic factors angiogenin; angiopoietin-1; G-CSF; PDGF-AA; PDGF-AB; PDGF-BB; PlGF; SCF; Tie-2; VEGF A; and VEGF D; (ii) the pro-inflammatory and/or chemotactic factors BLC; ENA-78; Eotaxin-3; G-CSF; GDF-15; I-309; IL-1α; IL-1β; IL-1ra; IL-2; IL-8; IL-11; IL-12p40; IL-12p70; IL-13R1; IL-13R2; IL-16; IL-17; IL-17B; IL-17F; IL-18BPa; IL-23; IL-28A; IP-10 (CXCL10); MCP-3; M-CSF; MIF; MIG; MIP-1δ; MIP-3α; MIP-3β; RANTES; SCF; ST2; TARC); (iii) and the proliferative growth factors BDNF; EGF; FGF-7; IGFBP-1; NrCAM; NT-3; NT-4; TGF-α; and TGF-β.
 25. The method of claim 23, wherein the induced factors indicating a host response to chemotherapy with paclitaxel or Folinic acid/Fluorouracil/Oxaliplatin (FOLFOX) include: (i) the pro-angiogenic factors SDF-1 and VEGF-C; (ii) the pro-inflammatory and/or chemotactic factors CXCL14 (BRAK); CXCL16; CXCR2 (IL-8 RB); CXCR6; GM-CSF; IL-1alpha; IL-1R4 (ST2); IL-3Ralpha; IL-7Ralpha; IL-9R; IL-10; IL-11; IL-12p70; IL-15; IL-15Ralpha; IL-17; IL-17R; IL-18R alpha; IL-20; IL-27; IL-28; IL-31; LIF; LIX; LRP-6; MadCAM-1; MCP-1; M-CSF; MIP-1gamma; MIP-2; TACI; and TARC; (iii) the proliferative growth factors IGFBP-1; TGF-beta1; and TGF-beta2; and (iv) the pro-metastatic factor MMP-9.
 26. The method of claim 22, wherein the cancer therapy is targeted therapy with the protease inhibitor bortezomib, and the induced factors indicating a host response to therapy with bortezomib include: (i) the pro-angiogenic factors PlGF-2 and VEGF-D; (ii) the pro-inflammatory and/or chemotactic factors CCL28; IL-1alpha; IL-1R4 (ST2); IL-3; IL-5; IL-6; IL-6R; IL-10; IL-11; IL-12p70; IL-13; IL-17C; IL-17E; IL-31; MCP-1; M-CSF; and MIP-3beta; and (iii) the proliferative growth factors IGFBP-1; IGFBP-3; and TGF-beta
 3. 27. The method of claim 22, wherein the cancer therapy is radiation therapy alone and the induced factors indicating a host response to radiation therapy include: (i) the pro-angiogenic factors angiogenin; angiopoietin-1; PDGF-AA; PDGF-BB; PLGF-2; SDF-1; (ii) the pro-inflammatory and/or chemotactic factors IL-10; MCP-1; and (iii) the proliferative growth factors EGF; FGF-1.
 28. The method of claim 22, wherein the cancer therapy is surgery alone and the induced factors indicating host response to surgery include: (i) the pro-angiogenic factors angiopoietin-1; PDGF-AA; PDGF-BB; and PLGF-2; and (ii) the pro-inflammatory and/or chemotactic factor MCP-1.
 29. A kit comprising a plurality of antibodies, at least part of the antibodies of the plurality of antibodies each selectively binding to each of a plurality of factors that promote responsiveness or non-responsiveness of a cancer patient to treatment with a cancer therapy, and instructions for use.
 30. The kit of claim 29, wherein said kit is a sandwich or an enzyme-linked immunosorbent assay (ELISA) kit.
 31. The kit of claim 29, comprising a plurality of human monoclonal antibodies, at least part of them each binding specifically to a pro-tumorigenic or pro-metastatic factor, wherein the pro-tumorigenic factors have pro-angiogenic, pro-inflammatory/chemotactic, or proliferative activity, at least some of these pro-tumorigenic and pro-metastatic factors being predictive of a favorable or a non-favorable response of a cancer patient to treatment with a cancer therapy.
 32. The kit of claim 31, wherein the monoclonal antibodies each specifically binds to a factor selected from the factors angiogenin; angiopoietin-1; angiopoietin-2; bNGF; cathepsin S; Galectin-7; GCP-2; G-CSF; GM-CSF; PAI-1; PDGF-AA; PDGF-BB; PDGF-AB; PlGF; PlGF-2; SDF-1; Tie2; VEGF-A; VEGF-C; VEGF-D; VEGF-R1; VEGF-R2; VEGF-R3; 6Ckine; angiopoietin-1; angiopoietin-2; BLC; BRAK; CD186; ENA-78; Eotaxin-1; Eotaxin-2; Eotaxin-3; EpCAM; GDF-15; GM-CSF; GRO; HCC-4; I-309; IFN-γ; IL-1α; IL-1β; IL-1R4 (ST2); IL-2; IL-2R; IL-3; IL-3Rα; IL-5; IL-6; IL-6R; IL-7; IL-8; IL-8 RB; IL-11; IL-12; IL-12p40; IL-12p70; IL-13; IL-13 R1; IL-13R2; IL-15; IL-15Rα; IL-16; IL-17; IL-17C; IL-17E; IL-17F; IL-17R; IL-18; IL-18BPa; IL-18 Rα; IL-20; IL-23; IL-27; IL-28; IL-31; IL-33; IP-10; I-TAC; LIF; LIX; LRP6; MadCAM-1; MCP-1; MCP-2; MCP-3; MCP-4; M-CSF; MIF; MIG; MIP-1 gamma; MIP-1α; MIP-1β; MIP-1δ; MIP-3α; MIP-3β; MPIF-1; PARC; PF4; RANTES; Resistin; SCF; SCYB16; TACI; TARC; TSLP: TNF-α; TNF R1; TRAIL-R4; TREM-1; Activin A; Amphiregulin; Axl; BDNF; BMP4; cathepsin S; EGF; FGF-1; FGF-2; FGF-7; FGF-21; Follistatin; Galectin-7; Gas6; GDF-15; HB-EGF; HGF; IGFBP-1; IGFBP-3; LAP; NGF R; NrCAM; NT-3; NT-4; PAI-1; TGF-α; TGF-β; TGF-β3; TRAIL-R4; ADAMTS1; cathepsin S; FGF-2; Follistatin; Galectin-7; GCP-2; GDF-15; IGFBP-6; LIF; MMP-9; pro-MMP9; RANK; RANKL; RANTES; SDF-1; and CXCR4.
 33. A method of treating a cancer patient with a cancer therapy, the method comprising the steps of: (i) performing an assay on a biological sample selected from blood plasma, whole blood, blood serum or peripheral blood mononuclear cells obtained from the cancer patient at a time period after a session of treatment with said cancer therapy, to determine the levels of one or more of a plurality of factors induced in the circulation of said cancer patient in response to treatment with said cancer therapy, said one or more of the plurality of factors promoting responsiveness or non-responsiveness of the cancer patient to the treatment with said cancer therapy; (ii) obtaining reference levels for each of the one or more of the plurality of the induced factors of step (i) in a biological sample selected from blood plasma, whole blood, blood serum or peripheral blood mononuclear cells, obtained from the cancer patient before said session of treatment with the cancer therapy; (iii) establishing the fold change for each of the one or more of the plurality of the induced factors of step (i) by comparing the level of each induced factor of step (i) with the reference level of step (ii) for the same factor; (iv) determining that the cancer patient has a favorable or a non-favorable response to the treatment with said cancer therapy based on the fold change established in step (iii) for one or more of the plurality of induced factors of step (i); and (iva) if the cancer patient has a non-favorable response to the treatment with said cancer therapy based on the fold change established in (iii) for one or more of the plurality of the induced factors, then selecting a dominant factor among the one or more factors showing a fold change indicative of said non-favorable response, and treating the patient with the cancer therapy in combination with an agent that neutralizes or blocks the dominant factor; or (ivb) if the cancer patient has a favorable response to the treatment with said cancer therapy based on the fold change of the level of the one or more factors established in (iii), then continuing the treatment of the cancer patient with the same cancer therapy.
 34. The method of claim 33, wherein the biological sample of steps (i) and (ii) is blood plasma.
 35. The method of claim 33, wherein said session of treatment with the cancer therapy is the first session of treatment with said cancer therapy, the biological sample of step (i) is obtained from the cancer patient at about 20, 24, 30, 36, 40, 48, 50, 60, 72 hours or more, including up to one to three weeks or more, after said first session of treatment, and the reference biological sample of step (ii) is obtained from the cancer patient at a time point including at about 72 hours or less, including at about 60, 50, 48, 40, 36, 30, 24 or 20 hours or just before said first session of treatment with the cancer therapy.
 36. The method of claim 34, wherein said session of treatment with the cancer therapy is one of multiple sessions of treatment that is not the first session of treatment with the cancer therapy, and the biological sample is obtained from the cancer patient at any time point between two consecutive sessions of treatment, wherein said biological sample is simultaneously the biological sample of step (i) and the reference biological sample according to step (ii) for the next session assay according to step (i).
 37. The method of claim 36, wherein the time between two consecutive sessions of treatment is from one day to 1 or 3 weeks, depending on the cancer therapy, and the biological sample is obtained from the cancer patient at about 20, 24, 30, 36, 40, 48, 50, 60, 72 hours or more, including up to one to three weeks or more, after the session of treatment that is not the first session of treatment with the cancer therapy.
 38. The method of claim 33, wherein the fold-change established in step (iii) is defined by a fold change of ≥1.5 indicating upregulation or a fold change of ≤0.5 indicating down-regulation in the level of each of the one or more of the plurality of factors induced in the circulation of the cancer patient in response to the treatment with the cancer therapy, these values being considered significant and predictive of a non-favorable or favorable response of the cancer patient to the treatment with said cancer therapy.
 39. The method of claim 38, wherein the prediction of a favorable or a non-favorable response of the cancer patient to the treatment with the cancer therapy is based on significant fold changes of one or more, optionally two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, or 20-25 or more, of the induced factors.
 40. The method of claim 33, wherein the factors induced in the circulation of said cancer patient in response to treatment with said cancer therapy are molecular factors including cytokines, chemokines, growth factors, enzymes and soluble receptors.
 41. The method of claim 40, wherein the factors are pro-tumorigenic or pro-metastatic factors, and the pro-tumorigenic factors may be pro-angiogenic, pro-inflammatory/chemotactic or proliferative growth factors.
 42. The method of claim 41, wherein if there is an increase (up-regulation) of at least about 1.5-fold in the level of one or more of the pro-tumorigenic factors, then the prediction is of a non-favorable response of the cancer patient to the treatment with the cancer therapy, and if there is a decrease (down-regulation) of at least about 0.5-fold in the level of one or more of the pro-tumorigenic factors, then the prediction is of a favorable response of the cancer patient to the treatment with the cancer therapy.
 43. The method of claim 33, wherein the selected dominant factor shows a fold change of ≥1.5 indicative of a non-favorable response of the cancer patient to the treatment with the cancer therapy, and proceeding with the treatment of the patient with said cancer therapy in combination with an agent that blocks said dominant factor or the receptor thereof.
 44. The method of claim 43, wherein the dominant factor is selected from factors including EGF, EGFR, FGF, IFN-γ, IL-1β, IL-2, IL-6, PDGF, TNF-α and VEGF-A.
 45. The method of claim 44, wherein the dominant factor is IL-1β, the cancer therapy is chemotherapy, and the cancer patient is treated with chemotherapy in combination with an agent that blocks the activity of IL-1β or blocks its receptor IL-1R, said agent including: (a) an IL-1 receptor antagonist (IL-1Ra); (b) a soluble decoy IL-1 type II receptor; (c) an anti-IL-1β neutralizing monoclonal antibody; (d) an anti-IL-1R neutralizing monoclonal antibody; (e) an IL-1β-converting enzyme (ICE) inhibitor; and (0 an IL-1β vaccine.
 46. The method of claim 44, wherein the dominant factor is IL-6, the cancer therapy is chemotherapy, and the cancer patient is treated with chemotherapy in combination with: (a) an agent that blocks the activity of IL-6, said agent including a human or humanized monoclonal antibody such as Siltuximab, Clazakizumab, Olokizumab, Elsilimomab, or Sirukumab; or (b) an agent that blocks the receptor IL-6R, said agent including a human or humanized monoclonal antibody such as Tocilizumab, Sarilumab or a nanobody such as Vobarilizumab. 